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Tricks or Traps? A Deep Dive into RL for LLM Reasoning

ICLR 2026 · Large language models › Reasoning and chain of thought · approach: RL with verifiable rewards for reasoning

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Systematically reproduces and isolates common RL-for-LLM techniques (normalization, clip-higher, loss aggregation, overlong filtering) on Qwen3 base and aligned models, gives guidelines, and proposes Lite PPO (group-mean/batch-std advantage normalization + token-level loss with vanilla PPO loss).

Problem

Conflicting and fragmented recommendations for RL techniques in LLM reasoning, with inconsistent experimental settings, data, and initialization making it unclear which tricks suit which setups.

Approach

Controlled, isolated ablations of normalization, clipping, loss aggregation and overlong filtering within one open-source framework (ROLL) across data difficulty, model size and base vs. aligned initialization; combines the two most effective techniques (group-mean/batch-std advantage normalization and token-level loss) into a critic-free PPO variant called Lite PPO.

Key results

• Lite PPO (group-mean, batch-std normalization plus token-level loss, vanilla PPO loss) outperforms GRPO and DAPO on non-aligned Qwen3 models, with a stable upward trend on small base models where the others collapse after peaking.
• Removing the std term from batch-level normalization stabilizes training on easy data where rewards are concentrated, while the choice matters little on hard data.
• Clip-higher helps aligned models (reducing entropy collapse) but has little or negative effect on base models; for 4B the best upper clip was 0.32 while 8B peaked at 0.28.

Surprising findings

• Clip-higher gives negligible or harmful effects on base models, the opposite of its benefit for aligned models.
• Token-level loss helps base models but sequence-level aggregation outperforms it on aligned models.

Limitations

• Experiments restricted to the Qwen3 model family; conclusions may not transfer to other LLM families.
• Evaluation limited to math reasoning benchmarks.
• Some trends (e.g., clip scaling law) are described as tentative ("appears to be").

Experimental setup

Datasets: SimpleRL-Zoo-Data DeepMath-103k

Benchmarks: MATH-500 OlympiadBench MinervaMath AIME24 AIME25 AMC23

Metrics: accuracy response length entropy

Baselines: GRPO DAPO REINFORCE++ RLOO

Builds on: PPO GRPO DAPO REINFORCE++ Dr. GRPO

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