Xiang-Nan He

LLM agents achieve substantial performance gains by jointly learning credit at both the turn and token generation levels, overcoming limitations of existing single-level credit assignment methods.

RePolicy achieves superior safety-policy invocation in language model agents, adapting dynamically to changing contexts and unseen trajectories.