Co-Evolving Acting and Forecasting for Language Agents.

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 inter