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News/TSPO: Breaking the Double Homogenization Dilemma in Multi-turn Search Policy Optimization
arxiv
PublishedApril 7, 2026 at 4:00 AM
▲bullish

TSPO: Breaking the Double Homogenization Dilemma in Multi-turn Search Policy Optimization

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Publisher summary· verbatim

arXiv:2601.22776v2 Announce Type: replace Abstract: Multi-turn tool-integrated reasoning enables Large Language Models (LLMs) to solve complex tasks through iterative information retrieval. However, current reinforcement learning (RL) frameworks for search-augmented reasoning predominantly rely on s

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Mentioned models
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#reinforcement learning#large language models#reasoning#optimization

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arxivWhen Generated Information Returns: Error Persistence in LLM-Mediated Information Ecosystems15harxivQuery-Driven Learning for Lifted Reasoning15harxivThe Social World Model Evaluation Gap15h
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