arxiv
PublishedApril 6, 2026 at 4:00 AM
Co-Evolution of Policy and Internal Reward for Language Agents
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arXiv:2604.03098v1 Announce Type: new Abstract: Large language model (LLM) agents learn by interacting with environments, but long-horizon training remains fundamentally bottlenecked by sparse and delayed rewards. Existing methods typically address this challenge through post-hoc credit assignment o
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