license: mit
task_categories:
- text-generation
language:
- en
tags:
- collabllm
- multiturn
- rl
- grpo
size_categories:
- 1K<n<10K
configs:
- config_name: default
data_files:
- split: train
path: rl_train.parquet
- split: validation
path: rl_validation.parquet
CollabLLM medium — RL (GRPO) train/validation split
Inputs for GRPO training on the CollabLLM medium document-writing task, as used to produce
the verl-grpo-medium-qwen3-4b-step{50,100,129} checkpoints.
| file | rows | size |
|---|---|---|
rl_train.parquet |
2072 | 16M |
rl_validation.parquet |
— | 2.2M |
Derived from the CollabLLM medium task (source articles: Kamaljp/medium_articles). Each row is
a single-turn prompt that the training loop expands into a multi-turn conversation with a
simulated user, then scores with an LLM judge.
⚠️ Known issue: train/eval overlap causes label leakage through the reward
Measured, not suspected. 35 of the 100 medium test prompts also appear in rl_train.parquet,
and on those rows the BLEU reward target is byte-identical to the test reference document.
Why this matters more for RL than for SFT: in supervised fine-tuning the reference never enters
the training text, so an overlap is only topical leakage. In RL the reference is the reward
target for the bleu_score metric, so an overlapping row leaks the eval label directly through
the reward signal.
Root cause: the upstream release sampled the example pool before applying its own train/test split, so the split does not separate them.
If you train on this, hold out the overlapping prompts, or report numbers on the non-overlapping 65. The overlap is a property of this file and travels with it.
Reproducing
The reward is a weighted sum of bleu_score (+1), interactivity (+1) and token_amount
(−0.1), with the judge and user simulator served as separate vLLM endpoints. Reference three-role
setting, thinking off everywhere:
| role | model |
|---|---|
| assistant (trained) | Qwen/Qwen3-4B |
| user simulator | Qwen/Qwen3.5-9B |
| judge / reward | Qwen/Qwen3.5-27B |
Full setup and the acceptance tests (5-step training replay, val@0 = 1.0618) are in
studynotes/new_cluster_onboarding.md in the code repo.
Note on the checkpoints
Training peaked at step 100 and regressed afterwards: step 129 has flat BLEU and
significantly worse interactivity. Report step 100. Do not pick a checkpoint on verl's
critic/rewards/mean — it credits brevity, which the benchmark judge penalises.