Tricks or Traps? A Deep Dive into RL for LLM Reasoning
This paper systematically reviews and evaluates widely adopted Reinforcement Learning (RL) techniques for Large Language Model (LLM) reasoning within a unified open-source framework. It addresses the lack of standardized guidelines and fragmented understanding by analyzing internal mechanisms and applicable scenarios. The study provides clear guidelines for practitioners and introduces "Lite PPO," a minimalist two-technique combination that outperforms more complex algorithms. ✨
Article Points:
1
RL4LLM lacks guidelines; this paper provides systematic evaluation and insights.
2
Advantage normalization (group mean, batch std) offers robust reward shaping.
3
Clip-Higher benefits aligned models by promoting exploration and preventing entropy collapse.
4
Token-level loss is effective for Base models, but less so for aligned (Instruct) models.
5
Overlong filtering helps short-to-medium reasoning, but not long-tail tasks.
6
Lite PPO, a minimalist two-technique combo, outperforms complex RL4LLM algorithms.
Tricks or Traps? A Deep Dive into RL for LLM Reasoning
Challenges
No std guidelines
Fragmented understanding
Conflicting conclusions
Systematic Review
Rigorous reproductions
Isolated evaluations
Unified open-source framework
Key Techniques
Normalization strategies
Clipping strategies
Loss aggregation
Filtering strategies
Guidelines
Technique preferences
Setup sensitivities
Reliable roadmap
Lite PPO
Minimalist two-technique combo
Advantage normalization
Token-level loss aggregation
Outperforms complex algorithms
Future Work
Monitor RL4LLM developments
Consolidate algorithms in ROLL
Explore streamlined RL algorithms
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