Co-Evolving Acting and Forecasting for Language Agents
Introduces a novel training technique that addresses sample efficiency bottlenecks in language agent development
AI Summary
Researchers propose Co-Evolving Acting and Forecasting (name), a method that improves language agent efficiency by adding multi-step action targets to standard RL approaches.
Excerpt
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 exploits this supervision through value targets that estimate per-step success probabilities or observation targets that fit environment response. Both are hard to construct well: (1) value targets come from the same single scalar signal for each step