<ul><li>Leveraging more test-time computation can enhance the reasoning capabilities of large language models (LLMs).</li><li>The verify-and-improve paradigm allows dynamic solution exploration and feedback incorporation for LLMs.</li><li>A new reinforcement learning algorithm called DPSDP is introduced to improve LLM performance by training an actor-critic system to refine answers iteratively.</li><li>Empirical results show that using DPSDP with various base models leads to enhancements on both in- and out-of-distribution benchmarks, demonstrating the benefits of multi-agent collaboration.</li></ul>

Reinforce LLM Reasoning through Multi-Agent Reflection

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