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RECON: Reasoning with Condensation for Efficient Retrieval-Augmented Generation
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Beyond Correctness: Rewarding Faithful Reasoning in Retrieval-Augmented Generation
Announce Type: replace Abstract: Inspired by the success of reinforcement learning (RL) in Large Language Model (LLM) training for domains like math and code, recent work has begun training LLMs to dynamically plan, query, and reason with search engines as tools -- a paradigm increasingly referred to as agentic search. Although these methods achieve performance improvement across popular short-form QA benchmarks, many prioritize final answer correctness while overlooking the quality of...
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