Year: 2024
Type: conference-paper
Abstract: Finetuning language agents with reasoningaction trajectories is effective, but obtaining these trajectories from human annotations or stronger models is costly and sometimes impractical.In this paper, we investigate the use of self-training in language agents, which can generate supervision from the agent itself, offering a promising alternative without relying on human or stronger model demonstrations.Self-training, however, requires high-quality model-generated samples, which are hard to obtain for challenging language agent tasks.To address this, we present Reflection-Reinforced Self-Training (Re-ReST), which uses a reflector to refine low-quality generated samples during self-training.The reflector takes the agent's output and feedback from an external environment (e.g., unit test results in code generation) to produce improved samples.This technique enhances the quality of inferior samples and efficiently enriches the self-training dataset with higher-quality samples.We conduct extensive experiments on open-source language agents across tasks, including multi-hop question answering, sequential decision-making, code generation, visual question answering, and text-toimage generation.The results demonstrate the effectiveness of self-training and Re-ReST in language agent tasks, with self-training improving baselines by 7.6% on HotpotQA and 28.4% on AlfWorld, and Re-ReST further boosting performance by 2.0% and 14.1%, respectively.Our studies also confirm the efficiency of using a reflector to generate high-quality samples for self-training.Moreover, we demonstrate a method to employ reflection during inference without ground-truth feedback, addressing the limitation of previous reflection work.
Institution: University of California, Los Angeles
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Awards: G5714225502, G8876996369
OpenAlex ID: W4404782171