<ul><li>Reinforcement learning has been used to enhance the reasoning capabilities of large language models, where an LLM generator is guided by a verifier.</li><li>Current RL post-training methods for LLMs often use fixed or discriminatively trained verifiers, which have limitations in reward hacking and generalization.</li><li>To address these issues, the Tango framework concurrently trains both an LLM generator and a process-level LLM verifier using RL in an interleaved manner.</li><li>The generative RL-trained verifier in Tango shows improved robustness and generalization, leading to state-of-the-art performance on math benchmarks and reasoning tasks.</li></ul>

RL Tango: Reinforcing Generator and Verifier Together for Language Reasoning

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