instruction stringlengths 19 155 | rejected stringlengths 25 408 | chosen stringlengths 100 789 |
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What distribution is used to sample the recurrence count during training? | The recurrence count during training is sampled from a log normal Poisson distribution. | The recurrence count r during training is randomized, sampled from a log normal Poisson distribution, per input sequence. |
How is backpropagation managed in relation to computational costs? | Backpropagation is truncated to only the last k iterations of the recurrent unit to manage computational costs. | To manage computational costs, backpropagation is truncated to only the last k iterations of the recurrent unit k 8 in experiments. |
What factors are mentioned that affect the stability of training? | Factors such as initialization, normalization, and hyperparameters affect the stability of training. | Factors like initialization, normalization and hyperparameters all matter, especially when scaling up the training. |
What should one be cautious about when applying optimization during RL training? | One should be very cautious when trying to apply optimization directly on CoT during RL training, or trying to avoid it altogether. | We would suggest being very cautious when trying to apply optimization directly on CoT during RL training, or trying to avoid it altogether. |
Who introduced the concept of Continuous Space Adaptive Computation Time? | The concept of Continuous Space Adaptive Computation Time was introduced by Alex Graves in 2016. | Thinking in Continuous Space Adaptive Computation Time, introduced by Alex Graves in 2016, predated large language models but pioneered the same direction of enabling the model to dynamically decide the number of computational steps to take at the inference time. |
What does the Universal Transformer combine? | The Universal Transformer combines self-attention in Transformer with the recurrent mechanism in RNN. | Universal Transformer Dehghani, et al. 2019 combines self attention in Transformer with the recurrent mechanism in RNN, dynamically adjusting the number of steps using adaptive computation time Graves, 2016. |
What is a key feature of the recent recurrent architecture design proposed by Geiping et al.? | A key feature of the recent recurrent architecture design proposed by Geiping et al. is the addition of a recurrent block R on top of the standard Transformer. | A recent recurrent architecture design, proposed by Geiping et al. 2025, adds a recurrent block R on top of the standard Transformer. |
How does the recurrent depth architecture function conceptually? | Conceptually, the recurrent depth architecture is similar to a conditioned diffusion model, where the original input is provided in every recurrent step while a random Gaussian initialized state gets updated iteratively. | Conceptually, this recurrent depth architecture is a bit similar to a conditioned diffusion model, where the original input mathbf e is provided in every recurrent step while a random Gaussian initialized state mathbf s _i gets updated iteratively through the process. |
What happened to the experiments that resembled diffusion models? | The experiments that resembled diffusion models turned out to be bad. | Interestingly some of their experiments of designs that resemble diffusion models more turned out to be bad. |
What is the randomization process for the recurrence count during training? | The recurrence count during training is randomized and sampled from a log normal Poisson distribution, per input sequence. | The recurrence count r during training is randomized, sampled from a log normal Poisson distribution, per input sequence. |
How is backpropagation handled in the experiments? | Backpropagation is truncated to only the last k iterations of the recurrent unit, where k is 8 in experiments. | To manage computational costs, backpropagation is truncated to only the last k iterations of the recurrent unit k 8 in experiments. |
What is the role of the embedding block during training? | The embedding block continues to receive gradient updates in every step since its output is injected in every step, mimicking RNN training. | The embedding block continues to receive gradient updates in every step since its output mathbf e is injected in every step, mimicking RNN training. |
What factors affect the stability of training a recurrent model? | Factors like initialization, normalization, and hyperparameters all matter, especially when scaling up the training. | Unsurprisingly, the stability of training a recurrent model turns out to be very sensitive. Factors like initialization, normalization and hyperparameters all matter, especially when scaling up the training. |
What issue was observed with certain designs resembling diffusion models? | Certain designs resembling diffusion models turned out to be bad during experiments. | Interestingly some of their experiments of designs that resemble diffusion models more turned out to be bad. |
How is the recurrence count during training managed? | The recurrence count during training is randomized and sampled from a log normal Poisson distribution for each input sequence. | The recurrence count r during training is randomized, sampled from a log normal Poisson distribution, per input sequence. |
What is done to manage computational costs during training? | To manage computational costs, backpropagation is truncated to only the last k iterations of the recurrent unit. | To manage computational costs, backpropagation is truncated to only the last k iterations of the recurrent unit k 8 in experiments. |
What factors affect the stability of training a recurrent model? | Factors such as initialization, normalization, and hyperparameters significantly affect the stability of training a recurrent model. | The stability of training a recurrent model turns out to be very sensitive. Factors like initialization, normalization and hyperparameters all matter, especially when scaling up the training. |
What can happen to hidden states during training? | Hidden states can collapse by predicting the same hidden state for every token, or the model may learn to ignore the incoming state. | For example, hidden states can collapse by predicting the same hidden state for every token or the model may learn to ignore the incoming state mathbf s. |
What is the suggestion regarding optimization on CoT during RL training? | It is suggested to be very cautious when trying to apply optimization directly on CoT during RL training, or trying to avoid it altogether. | We would suggest being very cautious when trying to apply optimization directly on CoT during RL training, or trying to avoid it altogether. |
Who introduced the concept of Continuous Space Adaptive Computation Time? | The concept of Continuous Space Adaptive Computation Time was introduced by Alex Graves in 2016. | Thinking in Continuous Space Adaptive Computation Time, introduced by Alex Graves in 2016, predated large language models but pioneered the same direction of enabling the model to dynamically decide the number of computational steps to take at the inference time. |
What are the two ways to enable adaptive thinking time in continuous space? | Adaptive thinking time in continuous space can be enabled vertically via recurrent architecture or horizontally via more sequential sampling steps. | Adaptive thinking time in continuous space can be enabled vertically via recurrent architecture or horizontally via more sequential sampling steps. |
What does the Universal Transformer combine? | The Universal Transformer combines self attention in Transformer with the recurrent mechanism in RNN, dynamically adjusting the number of steps using adaptive computation time. | Universal Transformer Dehghani, et al. 2019 combines self attention in Transformer with the recurrent mechanism in RNN, dynamically adjusting the number of steps using adaptive computation time Graves, 2016. |
What is a recent recurrent architecture design proposed by Geiping et al.? | A recent recurrent architecture design proposed by Geiping et al. adds a recurrent block R on top of the standard Transformer, where each iteration takes the embedding mathbf e and a random state mathbf s_i. | A recent recurrent architecture design, proposed by Geiping et al. 2025, adds a recurrent block R on top of the standard Transformer. Every iteration of this recurrent block takes the embedding mathbf e and a random state mathbf s _i. |
What is a potential issue with designs resembling diffusion models? | Some designs that resemble diffusion models turned out to be bad in experiments. | Interestingly some of their experiments of designs that resemble diffusion models more turned out to be bad. |
How is the recurrence count during training determined? | The recurrence count during training is randomized and sampled from a log normal Poisson distribution for each input sequence. | The recurrence count r during training is randomized, sampled from a log normal Poisson distribution, per input sequence. |
What technique is used to manage computational costs during training? | Backpropagation is truncated to only the last k iterations of the recurrent unit, making it possible to train on the heavy tail part of the Poisson distribution. | To manage computational costs, backpropagation is truncated to only the last k iterations of the recurrent unit k 8 in experiments. |
What factors affect the stability of training a recurrent model? | Factors like initialization, normalization, and hyperparameters are crucial for the stability of training a recurrent model. | The stability of training a recurrent model turns out to be very sensitive. Factors like initialization, normalization and hyperparameters all matter, especially when scaling up the training. |
What measures did Geiping et al. take to stabilize training? | Geiping et al. adopted an embedding scale factor, a small learning rate, and careful tuning to stabilize training. | To stabilize the training, Geiping et al. adopted an embedding scale factor, a small learning rate and careful tuning. |
What issue arises when incorporating CoT monitoring rewards into reinforcement learning? | Incorporating CoT monitoring rewards into reinforcement learning leads to obfuscated reward hacking, where the model tries to hide its true intent within the CoT. | However, incorporating CoT monitoring reward into RL does not work out well, as it leads to obfuscated reward hacking where the model tries to hide its true intent within the CoT. |
How does the CoT monitor affect the model's ability to hack during training? | The CoT monitor provides direct optimization pressure during training but the model can still find ways to hack, though less frequently, and can hide its intentions to avoid detection. | With this direct optimization pressure provided by the CoT monitor applied during RL training, the model still can find ways to hack, although a bit less frequently, but can hide its intention in CoT to avoid being caught by the monitor. |
What did Yeo et al. 2025 find regarding reward hacking in their experiments? | Yeo et al. 2025 confirmed that new types of reward hacking can occur with new types of reinforcement learning reward shaping. | Experiments on CoT length RL signals by Yeo et al. 2025 confirmed a similar message as Baker et al. that new types of reward hacking can happen with new types of RL reward shaping. |
How did Yeo et al. design the reward function for CoT? | Yeo et al. designed the reward function so that correct CoTs receive higher rewards than incorrect ones, with shorter correct CoTs obtaining higher rewards than longer correct ones, and shorter incorrect CoTs receiving higher penalties than longer incorrect ones. | They designed the reward function such that correct CoTs can have higher rewards than wrong ones, short correct CoTs obtain higher rewards than long correct ones and short incorrect CoT receive higher penalties than long incorrect CoTs. |
What issues were observed with designs resembling diffusion models? | Some of their experiments of designs that resemble diffusion models turned out to be bad. | Interestingly some of their experiments of designs that resemble diffusion models more turned out to be bad. |
How is the recurrence count during training managed? | The recurrence count during training is randomized, sampled from a log normal Poisson distribution, per input sequence. | The recurrence count r during training is randomized, sampled from a log normal Poisson distribution, per input sequence. |
What measures are taken to manage computational costs during training? | Backpropagation is truncated to only the last k iterations of the recurrent unit to manage computational costs. | To manage computational costs, backpropagation is truncated to only the last k iterations of the recurrent unit k 8 in experiments. |
What factors affect the stability of training a recurrent model? | Factors like initialization, normalization, and hyperparameters all matter, especially when scaling up the training. | The stability of training a recurrent model turns out to be very sensitive. Factors like initialization, normalization and hyperparameters all matter, especially when scaling up the training. |
What strategy did Geiping et al. adopt to stabilize training? | Geiping et al. adopted an embedding scale factor, a small learning rate, and careful tuning to stabilize the training. | To stabilize the training, Geiping et al. adopted an embedding scale factor, a small learning rate and careful tuning. |
What issues were encountered with designs resembling diffusion models? | Some experiments with designs that resemble diffusion models turned out to be bad. | Interestingly some of their experiments of designs that resemble diffusion models more turned out to be bad. |
How is the recurrence count during training managed? | The recurrence count during training is randomized and sampled from a log normal Poisson distribution, per input sequence. | The recurrence count r during training is randomized, sampled from a log normal Poisson distribution, per input sequence. |
What is truncated during backpropagation to manage computational costs? | Backpropagation is truncated to only the last k iterations of the recurrent unit to manage computational costs. | To manage computational costs, backpropagation is truncated to only the last k iterations of the recurrent unit k 8 in experiments. |
What strategies did Geiping et al. adopt to stabilize training? | Geiping et al. adopted an embedding scale factor, a small learning rate, and careful tuning to stabilize the training. | To stabilize the training, Geiping et al. adopted an embedding scale factor, a small learning rate and careful tuning. |
What factors can affect the stability of training a recurrent model? | Factors like initialization, normalization, and hyperparameters can affect the stability of training a recurrent model. | Factors like initialization, normalization and hyperparameters all matter, especially when scaling up the training. |
What issues were encountered with designs resembling diffusion models? | The experiments with designs resembling diffusion models turned out to be bad. | Interestingly some of their experiments of designs that resemble diffusion models more turned out to be bad. |
How is the recurrence count during training determined? | The recurrence count during training is randomized and sampled from a log normal Poisson distribution, per input sequence. | The recurrence count r during training is randomized, sampled from a log normal Poisson distribution, per input sequence. |
What is truncated during backpropagation to manage computational costs? | Backpropagation is truncated to only the last k iterations of the recurrent unit to manage computational costs. | To manage computational costs, backpropagation is truncated to only the last k iterations of the recurrent unit k 8 in experiments. |
What factors affect the stability of training a recurrent model? | Factors such as initialization, normalization, and hyperparameters are very sensitive and matter for the stability of training a recurrent model. | The stability of training a recurrent model turns out to be very sensitive. Factors like initialization, normalization and hyperparameters all matter, especially when scaling up the training. |
What measures were taken to stabilize the training process? | To stabilize the training, Geiping et al. adopted an embedding scale factor, a small learning rate, and careful tuning. | To stabilize the training, Geiping et al. adopted an embedding scale factor, a small learning rate and careful tuning. |
What challenges arise in training recurrent models? | Training recurrent models can be very sensitive due to factors like initialization, normalization, and hyperparameters. Additionally, hidden states can collapse, leading to the model predicting the same hidden state for every token. | The stability of training a recurrent model turns out to be very sensitive. Factors like initialization, normalization and hyperparameters all matter, especially when scaling up the training. For example, hidden states can collapse by predicting the same hidden state for every token. |
How is backpropagation managed during training? | Backpropagation is truncated to only the last k iterations of the recurrent unit to manage computational costs, allowing training on the heavy tail part of the Poisson distribution. | To manage computational costs, backpropagation is truncated to only the last k iterations of the recurrent unit k 8 in experiments, making it possible to train on the heavy tail part of the Poisson distribution. |
What is the purpose of the embedding block during training? | The embedding block continues to receive gradient updates in every step, which allows it to inject its output into every step, mimicking RNN training. | The embedding block continues to receive gradient updates in every step since its output mathbf e is injected in every step, mimicking RNN training. |
What are thinking tokens? | Thinking tokens are a set of implicit tokens introduced during training or inference that do not carry direct linguistic meaning. | Thinking tokens refer to a set of implicit tokens introduced during training or inference that do not carry direct linguistic meaning. |
What adjustments did Geiping et al. make to stabilize training? | Geiping et al. adopted an embedding scale factor, a small learning rate, and careful tuning to stabilize the training process. | To stabilize the training, Geiping et al. adopted an embedding scale factor, a small learning rate and careful tuning. |
What issues were observed with designs resembling diffusion models? | Some experiments with designs resembling diffusion models turned out to be bad. | Interestingly some of their experiments of designs that resemble diffusion models more turned out to be bad. |
What is the relationship between the recurrence count and training in the context provided? | The recurrence count during training is randomized and sampled from a log normal Poisson distribution for each input sequence. | The recurrence count r during training is randomized, sampled from a log normal Poisson distribution, per input sequence. |
What technique was used to manage computational costs during training? | Backpropagation was truncated to only the last k iterations of the recurrent unit to manage computational costs. | To manage computational costs, backpropagation is truncated to only the last k iterations of the recurrent unit k 8 in experiments. |
What factors affect the stability of training a recurrent model? | Factors such as initialization, normalization, and hyperparameters all matter and are especially important when scaling up the training. | The stability of training a recurrent model turns out to be very sensitive. Factors like initialization, normalization and hyperparameters all matter, especially when scaling up the training. |
What are 'thinking tokens' and what is their purpose? | 'Thinking tokens' are implicit tokens introduced during training or inference that provide extra thinking time and compute power for the model. | Thinking tokens refer to a set of implicit tokens introduced during training or inference that do not carry direct linguistic meaning. Instead, their role is to provide extra thinking time and compute power for the model to perform better. |
How do thinking tokens affect the performance of a model on a dataset? | Training with thinking tokens on a toy model setup results in lower perplexity compared to a baseline model trained without them. | Training with thinking tokens on a toy model setup results in lower perplexity than baseline model trained without them. |
In which scenarios are the benefits of thinking tokens more pronounced? | The benefits of thinking tokens are more pronounced for non-trivial reasoning tasks or sentences involving numbers. | The benefits of thinking tokens are more pronounced for non trivial reasoning tasks or sentences involving numbers. |
What is the function of pause tokens as proposed by Goyal et al. 2024? | Pause tokens delay the model's outputs by appending dummy tokens like a character or at the end of the input sequence, providing extra computation during inference. | Similarly, pause tokens proposed by Goyal et al. 2024 delay the model s outputs by appending dummy tokens e.g. character like . or at the end of the input sequence, giving the model extra computation during inference. |
What method is used for inserting pause tokens during training? | During training, multiple copies of pause tokens are inserted at uniformly random locations, and the loss on pause tokens is ignored for training. | During training, multiple copies of pause tokens are inserted at uniformly random locations and the loss on pause tokens is ignored for training. |
What are thinking tokens and their purpose in model training? | Thinking tokens are special tokens inserted after each word in a sentence during model training. They provide extra time for the model to process information and make better predictions. | Herel Mikolov 2023 introduced the idea of inserting special thinking tokens T after each word in a sentence and training the model on such a dataset. Each thinking token buys extra time for the model to process and make better predictions. |
How do thinking tokens affect model performance compared to baseline models? | Training with thinking tokens on a toy model setup results in lower perplexity compared to baseline models that are trained without them. | Training with thinking tokens on a toy model setup results in lower perplexity than baseline model trained without them. |
What is the significance of pause tokens during model training? | Pause tokens delay the model's outputs by appending dummy tokens at the end of the input sequence, allowing for extra computation during both training and inference. | Similarly, pause tokens proposed by Goyal et al. 2024 delay the model s outputs by appending dummy tokens e.g. character like . or at the end of the input sequence, giving the model extra computation during inference. |
What benefits do thinking tokens and pause tokens provide during model inference? | Both thinking tokens and pause tokens help expand computation by introducing more inference loops, effectively increasing the model's computational capacity. | On one hand, it helps expand the computation by introducing more inference loops, effectively increasing computational capacity. |
What are thinking tokens and how do they affect model performance? | Thinking tokens are special tokens inserted after each word in a sentence to provide the model with extra time for processing. Training with these tokens leads to lower perplexity, particularly benefiting non-trivial reasoning tasks or sentences involving numbers. | Herel Mikolov 2023 introduced the idea of inserting special thinking tokens T after each word in a sentence and training the model on such a dataset. Each thinking token buys extra time for the model to process and make better predictions. Training with thinking tokens on a toy model setup results in lower perplexity t... |
What are pause tokens and what is their purpose? | Pause tokens are dummy tokens appended at the end of the input sequence to delay the model's outputs, allowing for extra computation during inference. | Similarly, pause tokens proposed by Goyal et al. 2024 delay the model s outputs by appending dummy tokens e.g. character like . or at the end of the input sequence, giving the model extra computation during inference. |
How should pause tokens be used during training and inference? | Pause tokens should be injected during both training and inference for optimal performance, as only fine-tuning on pause tokens yields limited gains. | It is important to inject such pause tokens both during training and inference time, while only fine tuning on pause tokens leads to limited gain. |
What method is used to insert pause tokens during training? | During training, multiple copies of pause tokens are inserted at randomly chosen locations in the input, and the loss on these tokens is ignored. | During training, multiple copies of pause tokens are inserted at uniformly random locations and the loss on pause tokens is ignored for training. |
What is the source of the illustration regarding pause tokens? | The illustration of how pause tokens are injected during training and inference is sourced from Goyal et al. | Illustration of how pause tokens are injected during training and inference in comparison to standard setup. Image source Goyal et al. |
What are thinking tokens and their purpose in model training? | Thinking tokens are special tokens inserted after each word in a sentence to allow the model extra time to process information and improve predictions during training. | Herel Mikolov 2023 introduced the idea of inserting special thinking tokens T after each word in a sentence and training the model on such a dataset. Each thinking token buys extra time for the model to process and make better predictions. |
How do thinking tokens affect model performance compared to baseline models? | Training with thinking tokens leads to lower perplexity than models trained without them, especially in non-trivial reasoning tasks or sentences involving numbers. | Training with thinking tokens on a toy model setup results in lower perplexity than baseline model trained without them. The benefits of thinking tokens are more pronounced for non trivial reasoning tasks or sentences involving numbers. |
What are pause tokens and their role during model inference? | Pause tokens delay the model's outputs by appending dummy tokens, providing extra computation during inference, and should be injected during both training and inference. | Similarly, pause tokens proposed by Goyal et al. 2024 delay the model s outputs by appending dummy tokens e.g. character like . or at the end of the input sequence, giving the model extra computation during inference. |
What is the effect of injecting pause tokens only during fine-tuning? | Injecting pause tokens only during fine-tuning leads to limited gains in model performance. | It is important to inject such pause tokens both during training and inference time, while only fine tuning on pause tokens leads to limited gain. |
What are the computational advantages of using thinking and pause tokens? | Thinking and pause tokens help expand computation by creating more inference loops, effectively increasing the computational capacity of the model. | On one hand, it helps expand the computation by introducing more inference loops, effectively increasing computational capacity. |
What are thinking tokens and how do they affect model training? | Thinking tokens are special tokens inserted after each word in a sentence to give the model extra time to process information. They help in making better predictions and result in lower perplexity when training on a dataset. | Herel Mikolov 2023 introduced the idea of inserting special thinking tokens T after each word in a sentence and training the model on such a dataset. Each thinking token buys extra time for the model to process and make better predictions. |
What results were observed when using thinking tokens on a toy model setup? | Training with thinking tokens on a toy model setup leads to lower perplexity compared to a baseline model that is trained without them. | Training with thinking tokens on a toy model setup results in lower perplexity than baseline model trained without them. |
What are the benefits of using thinking tokens compared to other tasks? | The benefits of thinking tokens are more pronounced for non-trivial reasoning tasks or sentences that involve numbers. | The benefits of thinking tokens are more pronounced for non trivial reasoning tasks or sentences involving numbers. |
What are pause tokens and how do they function during model inference? | Pause tokens are dummy tokens that delay the model's outputs by appending characters at the end of the input sequence, providing extra computation time during inference. | Similarly, pause tokens proposed by Goyal et al. 2024 delay the model s outputs by appending dummy tokens e.g. character like . or at the end of the input sequence, giving the model extra computation during inference. |
What is the significance of injecting pause tokens during training and inference? | Injecting pause tokens during both training and inference is important, as only fine-tuning on pause tokens results in limited gain. | It is important to inject such pause tokens both during training and inference time, while only fine tuning on pause tokens leads to limited gain. |
What are thinking tokens and their purpose in model training? | Thinking tokens are special tokens inserted after each word in a sentence that help the model process and make better predictions by buying extra time. They lead to lower perplexity when training models, especially in non-trivial reasoning tasks. | Herel Mikolov 2023 introduced the idea of inserting special thinking tokens T after each word in a sentence and training the model on such a dataset. Each thinking token buys extra time for the model to process and make better predictions. |
How do thinking tokens affect model performance on certain tasks? | The benefits of thinking tokens are more pronounced for non-trivial reasoning tasks or sentences involving numbers, leading to improved model performance in these areas. | The benefits of thinking tokens are more pronounced for non trivial reasoning tasks or sentences involving numbers. |
What are pause tokens and how do they function during inference? | Pause tokens are dummy tokens that delay the model's outputs by appending characters at the end of the input sequence, providing extra computation time during inference. | Similarly, pause tokens proposed by Goyal et al. 2024 delay the model s outputs by appending dummy tokens e.g. character like . or at the end of the input sequence, giving the model extra computation during inference. |
What is the significance of injecting pause tokens during training and inference? | Injecting pause tokens during both training and inference is crucial, as fine-tuning only on pause tokens results in limited gains in model performance. | It is important to inject such pause tokens both during training and inference time, while only fine tuning on pause tokens leads to limited gain. |
What is the effect of using thinking and pause tokens on computational capacity? | Both thinking and pause tokens help expand computational capacity by introducing more inference loops, even though they do not carry extra information or add many new parameters. | Interestingly, thinking tokens or pause tokens in above experiments do not carry any extra information or add many new parameters. But why is it still helpful? On one hand, it helps expand the computation by introducing more inference loops, effectively increasing computational capacity. |
What are thinking tokens and how do they affect model training? | Thinking tokens are special tokens inserted after each word in a sentence that allow the model to have extra time to process information, resulting in lower perplexity during training compared to models without them. | Herel Mikolov 2023 introduced the idea of inserting special thinking tokens T after each word in a sentence and training the model on such a dataset. Training with thinking tokens on a toy model setup results in lower perplexity than baseline model trained without them. |
What is the purpose of pause tokens in model training? | Pause tokens are used to delay a model's outputs by appending dummy tokens, providing the model with extra computation time during inference. | Similarly, pause tokens proposed by Goyal et al. 2024 delay the model s outputs by appending dummy tokens e.g. character like . or at the end of the input sequence, giving the model extra computation during inference. |
How are pause tokens integrated during training? | During training, multiple copies of pause tokens are inserted at random locations within the input, and the loss on pause tokens is ignored for training. | During training, multiple copies of pause tokens are inserted at uniformly random locations and the loss on pause tokens is ignored for training. |
What benefits do thinking tokens and pause tokens provide despite not adding extra information? | Both thinking tokens and pause tokens help to expand computation by introducing more inference loops, thereby increasing the computational capacity of the model. | On one hand, it helps expand the computation by introducing more inference loops, effectively increasing computational capacity. |
What does Quiet STaR introduce in terms of token-level reasoning? | Quiet STaR introduces token-level reasoning by training the model to generate rationales after every token, explaining future text. | Quiet STaR Zelikman et al. 2025 introduces token level reasoning by training the model to generate rationales after every token to explain future text. |
What are thinking tokens and how do they affect model training? | Thinking tokens are special tokens inserted after each word in a sentence to give the model extra time for processing. Their use in training results in lower perplexity compared to models trained without them. | Herel Mikolov 2023 introduced the idea of inserting special thinking tokens T after each word in a sentence and training the model on such a dataset. Training with thinking tokens on a toy model setup results in lower perplexity than baseline model trained without them. |
What is the purpose of pause tokens in model training? | Pause tokens are used to delay the model's outputs by appending dummy tokens at the end of the input sequence, allowing the model extra computation during inference. | Similarly, pause tokens proposed by Goyal et al. 2024 delay the model s outputs by appending dummy tokens e.g. character like . or at the end of the input sequence, giving the model extra computation during inference. |
What is the benefit of using thinking tokens or pause tokens according to the experiments? | Thinking tokens and pause tokens help expand computation by introducing more inference loops, effectively increasing computational capacity without carrying extra information or new parameters. | Interestingly, thinking tokens or pause tokens in above experiments do not carry any extra information or add many new parameters. But why is it still helpful? On one hand, it helps expand the computation by introducing more inference loops, effectively increasing computational capacity. |
What limitation exists when fine-tuning with pause tokens? | Fine-tuning only on pause tokens leads to limited gain, suggesting that they should be injected during both training and inference time for better results. | It is important to inject such pause tokens both during training and inference time, while only fine tuning on pause tokens leads to limited gain. |
What are the three stages of the Quiet STaR process? | The three stages of the Quiet STaR process are Think, Talk, and Learn. | Quiet STaR consists of three stages _Think_ Predicting next tokens with rationales. _Talk_ Next token prediction without rationale is mixed with post rationale prediction. _Learn_ Train the model to generate better rationale via REINFORCE by learning from examples that increase the probability of correct next token whi... |
How does the Think stage of Quiet STaR operate? | In the Think stage, the system predicts next tokens with rationales, generating multiple rationales in parallel due to the high computational cost. | Think_ Predicting next tokens with rationales. Due to high computational cost demanded by token level reasoning, this process is designed to generate multiple rationales in parallel. |
What is the purpose of the mixing weight in the Talk stage? | The mixing weight in the Talk stage is learned by a special mixing head of a shallow MLP to combine next token prediction without rationale and post rationale prediction. | The mixing weight for two logits is learned by a special mixing head of a shallow MLP out of hidden output after each rationale. |
What technique is used in the Learn stage to improve rationale generation? | The Learn stage uses the REINFORCE technique to train the model to generate better rationales by learning from examples. | Learn_ Train the model to generate better rationale via REINFORCE by learning from examples that increase the probability of correct next token while discarding those that hurt the prediction. |
What allows thought tokens to manage their attention in the Think stage? | A special attention map is used in the Think stage to enable thought tokens to focus on themselves, preceding thought tokens, and prior text. | A special attention map is used to enable all thought tokens to only pay attention to themselves, all preceding thought tokens within the same thought, and the preceding text. |
What are the three stages of Quiet STaR? | The three stages of Quiet STaR are Think, Talk, and Learn. | Quiet STaR consists of three stages _Think_ Predicting next tokens with rationales. _Talk_ Next token prediction without rationale is mixed with post rationale prediction. _Learn_ Train the model to generate better rationale via REINFORCE by learning from examples that increase the probability of correct next token whi... |
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