Reinforce LLM Reasoning through Multi-Agent Reflection
This addresses the challenge of suboptimal performance in verify-and-improve methods for LLM reasoning, offering incremental improvements through better coordination and feedback utilization.
The paper tackles the problem of boosting LLM reasoning through multi-turn refinement by introducing DPSDP, a reinforcement learning algorithm that trains an actor-critic system to iteratively refine answers, resulting in improved accuracy from 58.2% to 63.2% on MATH 500 with Ministral-based models.
Leveraging more test-time computation has proven to be an effective way to boost the reasoning capabilities of large language models (LLMs). Among various methods, the verify-and-improve paradigm stands out for enabling dynamic solution exploration and feedback incorporation. However, existing approaches often suffer from restricted feedback spaces and lack of coordinated training of different parties, leading to suboptimal performance. To address this, we model this multi-turn refinement process as a Markov Decision Process and introduce DPSDP (Direct Policy Search by Dynamic Programming), a reinforcement learning algorithm that trains an actor-critic LLM system to iteratively refine answers via direct preference learning on self-generated data. Theoretically, DPSDP can match the performance of any policy within the training distribution. Empirically, we instantiate DPSDP with various base models and show improvements on both in- and out-of-distribution benchmarks. For example, on benchmark MATH 500, majority voting over five refinement steps increases first-turn accuracy from 58.2% to 63.2% with Ministral-based models. An ablation study further confirms the benefits of multi-agent collaboration and out-of-distribution generalization.