Researchers have developed APIVIS, a novel framework designed to enhance the mathematical reasoning abilities of large language models. This system integrates finite-budget Gumbel search into reinforcement learning with verifiable rewards (RLVR) to increase the diversity of training rollouts. APIVIS combines direct and searched responses, ensuring that improvements discovered during search positively influence the model's policy. The framework also incorporates selective supervision to maintain a learning signal when group rewards become uniform, which can otherwise hinder the GRPO algorithm. Experiments on established mathematical reasoning benchmarks show that APIVIS significantly outperforms existing search-based methods. AI
IMPACT Enhances LLM capabilities in mathematical reasoning, potentially improving performance in complex problem-solving tasks.
RANK_REASON The cluster describes a new research paper detailing a novel framework for improving LLM mathematical reasoning. [lever_c_demoted from research: ic=1 ai=1.0]
- APIVIS
- arXiv
- GRPO
- Gumbel search
- Hugging Face
- large-language models
- Reinforcement learning with verifiable rewards
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