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攻克AI推理难题,清华团队提出「统一LLM强化学习新范式」ReST-RL
3 6 Ke·2025-09-10 09:53

Core Insights - The article discusses the ongoing debate in the industry regarding the reasoning capabilities of large language models (LLMs), highlighting their frequent failures in complex tasks and the challenges in improving their reasoning abilities [1][3]. Group 1: Current Challenges in LLMs - Existing LLMs struggle with complex code, multi-step logic, and abstract tasks, often resulting in logical errors and irrelevant responses [1]. - Current reinforcement learning (RL) methods, such as online RL and self-training, have shown potential in enhancing LLM reasoning but face limitations in training efficiency and data collection costs [3][4]. - The reliance on high-quality labeled data for training process reward models (PRMs) restricts the scalability and reliability of these methods [4]. Group 2: Introduction of ReST-RL - Tsinghua University's KEG team proposed a new RL paradigm called ReST-RL, which combines an improved GRPO algorithm with a value model (VM) assisted decoding method to enhance LLM reasoning capabilities while maintaining efficiency and scalability [1][5]. - ReST-RL consists of two main components: ReST-GRPO, which optimizes the training process, and VM-MCTS, which aids in decoding during testing [5][9]. Group 3: Performance and Validation - Experimental results indicate that ReST-RL outperforms other RL baselines and decoding methods across various programming benchmarks, demonstrating its significant potential in enhancing LLM reasoning capabilities [2][10]. - ReST-GRPO improves training efficiency compared to original GRPO and DAPO, while VM-MCTS shows superior accuracy in validation tasks [10]. Group 4: Limitations and Future Directions - Despite the promising results, ReST-RL has not been validated in tasks beyond code reasoning, such as mathematical or commonsense reasoning, indicating a need for further research [13][14]. - The accuracy of the value model in out-of-domain tasks remains underexplored, suggesting that future work should focus on its generalization capabilities across a broader range of tasks [14].