Walk Before You Run! Concise LLM Reasoning via Reinforcement Learning
As test-time scaling becomes a pivotal research frontier in Large Language
Models (LLMs) development, contemporary and advanced post-training
methodologies increasingly focus on extending the generation length of long
Chain-of-Thought (CoT) responses to enhance reasoning capabilities toward
DeepSeek R1-like performance. However, recent studies reveal a persistent
overthinking phenomenon in state-of-the-art reasoning models, manifesting as
excessive redundancy or repetitive thinking patterns in long CoT responses. To
address this issue, in this paper, we propose a simple yet effective two-stage
reinforcement learning framework for achieving concise reasoning in LLMs, named
ConciseR. Specifically, the first stage, using more training steps, aims to
incentivize the model's reasoning capabilities via Group Relative Policy
Optimization with clip-higher and dynamic sampling components (GRPO++), and the
second stage, using fewer training steps, explicitly enforces conciseness and
improves efficiency via Length-aware Group Relative Policy Optimization
(L-GRPO). Significantly, ConciseR only optimizes response length once all
rollouts of a sample are correct, following the "walk before you run"
principle. Extensive experimental results demonstrate that our ConciseR model,
which generates more concise CoT reasoning responses, outperforms recent
state-of-the-art reasoning models with zero RL paradigm across AIME 2024,
MATH-500, AMC 2023, Minerva, and Olympiad benchmarks.
6836b5fccbd5554d2038732c
Mingyang Song
claimed_verified
2025-05-28T08:55:33.180Z
617051728db4a760d912d81f
https://cdn-avatars.huggingface.co/v1/production/uploads/617051728db4a760d912d81f/4dKG176GPk2pPRK7ctaT-.jpeg
Mingyang Song
user
Nickyang
6836b5fccbd5554d2038732d
Mao Zheng
Walk Before You Run! Concise LLM Reasoning via Reinforcement Learning
As test-time scaling becomes a pivotal research frontier in Large Language
Models (LLMs) development, contemporary and advanced post-training
methodologies increasingly focus on extending the generation length of long
Chain-of-Thought (CoT) responses to enhance reasoning capabilities toward
DeepSeek R1-like performance. However, recent studies reveal a persistent
overthinking phenomenon in state-of-the-art reasoning models, manifesting as
excessive redundancy or repetitive thinking patterns in long CoT responses. To
address this issue, in this paper, we propose a simple yet effective two-stage
reinforcement learning framework for achieving concise reasoning in LLMs, named
ConciseR. Specifically, the first stage, using more training steps, aims to
incentivize the model's reasoning capabilities via Group Relative Policy
Optimization with clip-higher and dynamic sampling components (GRPO++), and the
second stage, using fewer training steps, explicitly enforces conciseness and
improves efficiency via Length-aware Group Relative Policy Optimization
(L-GRPO). Significantly, ConciseR only optimizes response length once all
rollouts of a sample are correct, following the "walk before you run"
principle. Extensive experimental results demonstrate that our ConciseR model,
which generates more concise CoT reasoning responses, outperforms recent
state-of-the-art reasoning models with zero RL paradigm across AIME 2024,
MATH-500, AMC 2023, Minerva, and Olympiad benchmarks.
A reinforcement learning framework, ConciseR, is proposed to enhance the conciseness and efficiency of reasoning in LLMs through a two-stage optimization process.
Large Language Models Chain-of-Thought DeepSeek R1 reinforcement learning Group Relative Policy Optimization clip-higher dynamic sampling Length-aware Group Relative Policy Optimization concise reasoning rollouts AIME 2024 MATH-500 AMC 2023 Minerva Olympiad benchmarks