Yes — **mostly yes**. For this kind of setup, especially in an **OpenEnv / HF-hosted RL loop**, the biggest gains usually come **first** from the **environment and reward design**, not from “just training longer.” ## The priority order If your goal is to make the model genuinely good, I would think about it in this order: ### 1) Make sure the environment teaches the right behavior Your model can only learn what the environment makes legible. That means the env must: * present a task clearly * expose enough state/observation to act correctly * make success and failure distinguishable * avoid ambiguous or noisy outcomes If the environment is messy, underspecified, or inconsistent, the model will learn unstable behavior no matter how good the base model is. A good test is: > If a smart human saw the observation and the task, would the correct next action be reasonably clear? If not, the model is being asked to learn from a blurry signal. --- ### 2) Make the reward system correct before making it sophisticated This is usually the **highest-leverage piece**. A weak reward causes one of these: * the model learns nothing * the model learns the wrong shortcut * the model overfits to exploiting the reward * training becomes noisy and unstable What you want is a reward that is: * **aligned** with the real objective * **dense enough** to guide learning * **hard to game** * **consistent across episodes** ### Good reward design usually looks like: * strong reward for true success * small penalties for invalid/useless actions * optional intermediate rewards only if they truly correlate with final success ### Bad reward design usually looks like: * rewarding verbosity instead of correctness * rewarding format over substance * giving accidental reward to partial but wrong behavior * sparse reward with almost no signal until the very end If your model is not improving, reward quality is one of the first things I would inspect. --- ### 3) Verify the environment is actually learnable Some environments are technically valid but practically unlearnable. For example: * episode too long * too many action choices * delayed reward * observation missing crucial information * success depends on luck or hidden state You want an environment where: * random policy does badly * simple heuristic does somewhat better * trained model can clearly outperform the heuristic That progression is very important. --- ### 4) Only then think about model size and training duration People often jump to: * bigger base model * more steps * more compute But if the environment/reward are bad, more training just makes the model memorize bad incentives more efficiently. Once the env and rewards are solid, then model-side decisions matter more: * better base model * longer context * better prompting format * more training steps * better RL algorithm --- # What “good” means in practice Define it operationally. Not: * “it sounds smart” * “loss went down” But something like: * success rate on held-out episodes * average episode return * percent of tasks completed correctly * hallucination rate * faithfulness to evidence * number of invalid actions per episode * steps to completion A model is “good” when these improve on an evaluation set that it did not train on. --- # Where to focus first in your case Given that you are using a **hosted HF environment**, I would focus on these three things first: ## A) Environment setup Check whether: * observations are clear and stable * resets are deterministic enough * action schema is simple and well-defined * terminal conditions are correct * the environment is not too slow If the environment is slow or flaky, training becomes both expensive and noisy. ## B) Reward system This is probably the most important. Ask: * Does the reward really reflect the desired behavior? * Can the model “cheat” the reward? * Is the reward too sparse? * Are near-correct behaviors rewarded sensibly? * Are obviously bad actions penalized? ## C) Evaluation harness Before more training, create a small benchmark: * 20–50 representative tasks * run before training * run after training * compare success rate Without this, you will not know whether “good” actually improved. --- # A practical mental model Think of the full training system like this: ```text Base model = raw capability Environment = what the model experiences Reward = what the model is encouraged to repeat Training loop = how strongly it adapts ``` So: * if the **base model** is weak, ceiling is lower * if the **environment** is confusing, learning is harder * if the **reward** is wrong, learning goes in the wrong direction * if the **training loop** is short, learning may not happen enough But among those, for RL-style agent training, the most common failure is: > the model is optimizing the wrong thing because the reward/env contract is weak --- # What I would do next, concretely ## Phase 1 — validate the environment manually Run a handful of episodes yourself and inspect: * observation * chosen action * next observation * reward * done flag For each one, ask: * does this transition make sense? * would a good policy be able to infer what to do? ## Phase 2 — test a trivial baseline Try: * random policy * rule-based heuristic * prompt-only baseline without RL If RL cannot beat a simple heuristic, the problem is probably in env/reward design. ## Phase 3 — inspect reward traces For successful vs failed episodes, compare: * total reward * per-step reward * where penalties happen * whether false positives happen You want successful trajectories to have clearly better returns than bad ones. ## Phase 4 — only then scale training After that: * increase training steps * improve the base model * tune generation/action parameters * maybe change algorithm --- # Simple rule of thumb If you are asking: > “Should I focus on the environment setup / reward system?” My answer is: **Yes, first.** Because in agent/RL training, **reward and environment quality usually matter more than raw training volume in the early stages**. --- # When to focus more on the model itself Shift attention to the model when: * the environment is stable * rewards are clearly aligned * evaluation is in place * a heuristic baseline works * but the model still cannot learn or generalize That is when you ask: * is the base model too small? * is the action format too hard? * does it need SFT before RL? * do I need better demonstrations? * do I need more steps? And one important point: in many hackathon settings, the best path is often: **SFT first, RL second** Because RL works much better when the model already knows the basic format of the task. --- # My strongest recommendation Don’t ask “how do I make the model good?” as one giant question. Break it into: 1. **Can the environment be solved at all?** 2. **Does the reward favor the right behavior?** 3. **Can a simple baseline get some success?** 4. **Does the trained model outperform the baseline on held-out tasks?** If you want, send me: * the task definition, * observation format, * action format, * and current reward function, and I’ll tell you where I think the biggest leverage is: **env design, reward shaping, SFT, or more training**.