Reinforcement Learning for Long-Horizon Interactive LLM Agents
This addresses the challenge of improving task completion rates for interactive LLM agents in multi-domain environments, representing a novel application rather than an incremental improvement.
The paper tackles the problem of training interactive digital agents (IDAs) in stateful digital environments, where prior methods achieve less than half of tasks in benchmarks like AppWorld, and presents a reinforcement learning approach called LOOP that trains a 32-billion-parameter agent to outperform the larger OpenAI o1 agent by 9 percentage points (15% relative).
Interactive digital agents (IDAs) leverage APIs of stateful digital environments to perform tasks in response to user requests. While IDAs powered by instruction-tuned large language models (LLMs) can react to feedback from interface invocations in multi-step exchanges, they have not been trained in their respective digital environments. Prior methods accomplish less than half of tasks in sophisticated benchmarks such as AppWorld. We present a reinforcement learning (RL) approach that trains IDAs directly in their target environments. We formalize this training as a partially observable Markov decision process and derive LOOP, a data- and memory-efficient variant of proximal policy optimization. LOOP uses no value network and maintains exactly one copy of the underlying LLM in memory, making its implementation straightforward and as memory-efficient as fine-tuning a single LLM. A 32-billion-parameter agent trained with LOOP in the AppWorld environment outperforms the much larger OpenAI o1 agent by 9 percentage points (15% relative). To our knowledge, this is the first reported application of RL to IDAs that interact with a stateful, multi-domain, multi-app environment via direct API calls. Our analysis sheds light on the effectiveness of RL in this area, showing that the agent learns to consult the API documentation, avoid unwarranted assumptions, minimize confabulation, and recover from setbacks.