Prevalent reinforcement learning~(RL) methods for fine-tuning LLM reasoners, such as GRPO or Leave-one-out PPO, abandon the learned value function in favor of empirically estimated returns. This hinders test-time compute scaling that relies on using the value-function for verification. In this work, we propose RL\\(^V\\) that augments any “value-free” RL method by jointly training the LLM as both a reasoner and a generative verifier using RL-generated data, adding verification capabilities without significant overhead. Empirically, RL\\(^V\\) boosts MATH accuracy by over 20\\% with parallel sampling and enables \\(8-32\\times\\) efficient test-time compute scaling compared to the base RL method. RL\\(^V\\) also exhibits strong generalization capabilities for both easy-to-hard and out-of-domain tasks. Furthermore, RL\\(^V\\) achieves \\(1.2-1.6\\times\\) higher performance when jointly scaling parallel and sequential test-time compute with a long reasoning R1 model.<\/p>\n","protected":false},"excerpt":{"rendered":"
Prevalent reinforcement learning~(RL) methods for fine-tuning LLM reasoners, such as GRPO or Leave-one-out PPO, abandon the learned value function in favor of empirically estimated returns. This hinders test-time compute scaling that relies on using the value-function for verification. In this work, we propose RL that augments any “value-free” RL method by jointly training the LLM […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"COLM 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