Long-horizon credit assignment without explicit rewards

Develop training strategies within the early experience paradigm for language agents that effectively address long-horizon credit assignment in the absence of explicit reward signals, extending the current implicit world modeling and self-reflection approaches beyond short-horizon traces.

Background

The paper introduces the early experience paradigm for training language agents using interaction-derived future states without relying on external rewards. Two methods—implicit world modeling and self-reflection—are proposed and empirically validated across diverse environments.

While these approaches deliver strong improvements, they primarily operate on short-horizon traces. Scaling them to handle long-horizon dependencies and credit assignment without reward signals is identified as an unresolved issue that limits broader applicability.