Concise Reasoning via Reinforcement Learning
FreeReducing reasoning model verbosity via two-phase RL
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About Concise Reasoning via Reinforcement Learning
A research paper that identifies the root cause of excessive token usage in reasoning models as an optimization artifact from reinforcement learning loss minimization when models produce incorrect answers. It provides theoretical analysis of PPO and GRPO, demonstrates a correlation between conciseness and correctness, and proposes a two-phase RL procedure that reduces response length while maintaining or improving accuracy.
Key Features
Theoretical analysis of PPO and GRPO showing verbosity as optimization artifact
Two-phase reinforcement learning procedure to reduce response length
Discovery of correlation between conciseness and correctness across models
Secondary stage trained on small set of solvable problems preserves accuracy
Pros & Cons
Pros
- Addresses a significant practical problem of token waste in reasoning models
- Maintains or improves accuracy while reducing length
- Grounded in theoretical analysis of PPO/GRPO
Cons
- Secondary stage relies on small set of solvable problems, may not generalize
- GRPO exhibits collapse modes limiting reliability
- Paper is theoretical and may require further empirical validation
Best For
Reducing computational cost of large language model reasoningImproving efficiency of reasoning models in productionResearch into reinforcement learning optimization in language models
FAQ
What is the main contribution of this paper?
It identifies that verbosity in reasoning models is an optimization artifact from RL loss minimization on incorrect answers, and proposes a two-phase RL procedure to reduce response length while preserving accuracy.
Why do reasoning models produce verbose outputs?
The paper shows it is due to reinforcement learning loss minimization when models produce incorrect answers, which systematically drives toward longer outputs.
Does the proposed method reduce accuracy?
No, it preserves or improves accuracy while significantly reducing response length.