RetroAgent: From Solving to Evolving via Retrospective Dual Intrinsic Feedback
This addresses the challenge of enabling agents to adapt continually in complex interactive environments, offering a novel approach that enhances both task-solving and evolution, though it is incremental in building on existing RL and memory-augmented techniques.
The paper tackles the problem of limited exploration and inefficient experiential learning in reinforcement learning for large language model-based agents by introducing RetroAgent, an online RL framework that uses retrospective dual intrinsic feedback. The result is state-of-the-art performance, with improvements such as +18.3% on ALFWorld and +27.1% on Sokoban compared to existing methods.
Standard reinforcement learning (RL) for large language model (LLM)-based agents typically optimizes extrinsic task-success rewards, prioritizing one-off task solving over continual adaptation. As a result, agents may converge to suboptimal policies due to limited exploration, and accumulated experience remains implicitly stored in model parameters, hindering efficient experiential learning. Inspired by humans' capacity for retrospective self-improvement, we introduce RetroAgent, an online RL framework that enables agents to master complex interactive environments not only by solving, but also by evolving under the joint guidance of extrinsic task-success rewards and retrospective dual intrinsic feedback. Concretely, RetroAgent features a hindsight self-reflection mechanism that produces: (1) intrinsic numerical feedback, which tracks incremental subtask completion relative to prior attempts to reward promising exploration; and (2) intrinsic language feedback, which distills reusable lessons into a memory buffer retrieved via our proposed Similarity & Utility-Aware Upper Confidence Bound (SimUtil-UCB) strategy, jointly balancing relevance, utility, and exploration. Extensive experiments across four challenging agentic tasks show that RetroAgent achieves state-of-the-art (SOTA) performance, substantially outperforming RL fine-tuning, memory-augmented RL, exploration-guided RL, and meta-RL methods -- e.g., exceeding Group Relative Policy Optimization (GRPO)-trained agents by +18.3% on ALFWorld, +15.4% on WebShop, +27.1% on Sokoban, and +8.9% on MineSweeper -- while maintaining strong test-time adaptation and out-of-distribution generalization.