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News/Co-Evolving Acting and Forecasting for Language Agents
arxiv
PublishedOctober 16, 2026 at 4:00 AM

Co-Evolving Acting and Forecasting for Language Agents

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arXiv:2610.20597v2 Announce Type: replace Abstract: Training language agents is bottlenecked by the high cost of long-horizon rollouts, yet standard agentic reinforcement learning (RL) learns only from a terminal scalar reward, leaving the supervision in intermediate steps largely unused. Prior work

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