ARIA: Training Language Agents with Intention-Driven Reward Aggregation
Authors: name: Ruihan Yang, name: Yikai Zhang, name: Aili Chen, name: Xintao Wang, name: Siyu Yuan, name: Jiangjie Chen, name: Deqing Yang, name: Yanghua Xiao

Large language models (LLMs) have enabled agents to perform complex reasoning
and decision-making through free-form language interactions. However, in
open-ended language action environments (e.g., negotiation or question-asking
games), the action space can be formulated as a joint distribution over tokens,
resulting in an exponentially large action space. Sampling actions in such a
space can lead to extreme reward sparsity, which brings large reward variance,
hindering effective reinforcement learning (RL). To address this, we propose
ARIA, a method that Aggregates Rewards in Intention space to enable efficient
and effective language Agents training. ARIA aims to project natural language
actions from the high-dimensional joint token distribution space into a
low-dimensional intention space, where semantically similar actions are
clustered and assigned shared rewards. This intention-aware reward aggregation
reduces reward variance by densifying reward signals, fostering better policy
optimization. Extensive experiments demonstrate that ARIA not only
significantly reduces policy gradient variance, but also delivers substantial
performance gains of an average of 9.95% across four downstream tasks,
consistently outperforming offline and online RL baselines.