Harnessing Uncertainty: Entropy-Modulated Policy Gradients for
  Long-Horizon LLM Agents
Authors: name: Jiawei Wang, name: Jiacai Liu, name: Yuqian Fu, name: Yingru Li, name: Xintao Wang, name: Yuan Lin, name: Yu Yue, name: Lin Zhang, name: Yang Wang, name: Ke Wang

In long-horizon tasks, recent agents based on Large Language Models (LLMs)
face a significant challenge that sparse, outcome-based rewards make it
difficult to assign credit to intermediate steps. Previous methods mainly focus
on creating dense reward signals to guide learning, either through traditional
reinforcement learning techniques like inverse reinforcement learning or by
using Process Reward Models for step-by-step feedback. In this paper, we
identify a fundamental problem in the learning dynamics of LLMs: the magnitude
of policy gradients is inherently coupled with the entropy, which leads to
inefficient small updates for confident correct actions and potentially
destabilizes large updates for uncertain ones. To resolve this, we propose
Entropy-Modulated Policy Gradients (EMPG), a framework that re-calibrates the
learning signal based on step-wise uncertainty and the final task outcome. EMPG
amplifies updates for confident correct actions, penalizes confident errors,
and attenuates updates from uncertain steps to stabilize exploration. We
further introduce a bonus term for future clarity that encourages agents to
find more predictable solution paths. Through comprehensive experiments on
three challenging agent tasks, WebShop, ALFWorld, and Deep Search, we
demonstrate that EMPG achieves substantial performance gains and significantly
outperforms strong policy gradient baselines. Project page is at
https://empgseed-seed.github.io/