<ul><li>The shift towards training large language models using reinforcement learning on verifiable rewards has shown advancements in code and mathematical reasoning.</li><li>The current methodology is limited to tasks with rule-based answer verification and does not easily extend to real-world domains like chemistry, healthcare, engineering, law, biology, business, and economics.</li><li>A verifier-free method named VeriFree is proposed to extend training to general reasoning domains, bypassing answer verification and maximizing the probability of generating the reference answer directly.</li><li>Comparison with verifier-based methods shows that VeriFree offers practical benefits, reduced compute requirements, and performs well on evaluations across various benchmarks.</li></ul>

Reinforcing General Reasoning without Verifiers

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