Agent训练最容易踩的坑：Credit Assignment Is All You Need
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TL;DR - A practitioner's first-hand account (via PaperWeekly) arguing that credit assignment, not infra/data/recipe tweaks, is the core bottleneck when moving from reasoning-RL to agentic RL. It matters because it explains why agentic RL runs show "healthy" curves yet random, non-improving eval gains.
- Reasoning-RL vs agentic RL diverge: for reasoning models, hard problems + large group size + long training + low train/inference divergence and stable entropy suffice; in agentic tasks the author reports point gains that are "random, mostly noise" across TITO, seq/token-level (biased/unbiased) variants, KL, entropy bonuses, and data swaps.
- Two failure modes named: correct trajectories can contain bad behaviors that get reinforced (hurting harder tasks), and failed trajectories can contain correct reasoning + tool-call paths that critic-free GRPO penalizes indiscriminately.
- Proposed fix — partial credit assignment via PivotRL-style prefix replay: offline SFT filtering, pick a cut point (first-error-step detection or high-entropy branch points), replay the prefix as an unoptimized prompt, and only roll out/optimize the suffix under standard GRPO. Author claims significant, non-noisy gains on some benchmarks plus faster training.
- Scaling-up options if compute allows: tree rollout with pivot-node selection and q-value estimation, or a value-pretrain stage (even just on RL data), with a claim that IID value-pretrain/RL data also improved OOD results. Caveat: these are the author's informal experimental notes with no reported numbers, and one cited reference ([6] EVPO) carries an implausible arXiv ID.
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Agent训练最容易踩的坑：Credit Assignment Is All You Need
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Semantic Scholar citations 3 · Semantic Scholar influential citations 0
TL;DR - A practitioner's first-hand account (via PaperWeekly) arguing that credit assignment, not infra/data/recipe tweaks, is the core bottleneck when moving from reasoning-RL to agentic RL. It matters because it explains why agentic RL runs show "healthy" curves yet random, non-improving eval gains.
- Reasoning-RL vs agentic RL diverge: for reasoning models, hard problems + large group size + long training + low train/inference divergence and stable entropy suffice; in agentic tasks the author reports point gains that are "random, mostly noise" across TITO, seq/token-level (biased/unbiased) variants, KL, entropy bonuses, and data swaps.
- Two failure modes named: correct trajectories can contain bad behaviors that get reinforced (hurting harder tasks), and failed trajectories can contain correct reasoning + tool-call paths that critic-free GRPO penalizes indiscriminately.
- Proposed fix — partial credit assignment via PivotRL-style prefix replay: offline SFT filtering, pick a cut point (first-error-step detection or high-entropy branch points), replay the prefix as an unoptimized prompt, and only roll out/optimize the suffix under standard GRPO. Author claims significant, non-noisy gains on some benchmarks plus faster training.
- Scaling-up options if compute allows: tree rollout with pivot-node selection and q-value estimation, or a value-pretrain stage (even just on RL data), with a claim that IID value-pretrain/RL data also improved OOD results. Caveat: these are the author's informal experimental notes with no reported numbers, and one cited reference ([6] EVPO) carries an implausible arXiv ID.