Technology Category
Machine Learning: Reinforcement LearningMultiagent Systems: Multiagent LearningNatural Language Processing: Safety and Robustness
Can RL Improve Generalization of LLM Agents? An Empirical Study
🤖 AI Summary
This work addresses the lack of systematic evaluation of cross-environment generalization in existing Reinforcement Fine-Tuning (RFT) methods for large language model agents, particularly under unknown environments, heterogeneous observation spaces, and divergent action interfaces. We propose the first fine-grained generalization evaluation framework that characterizes RFT behavior under environmental distribution shifts across three dimensions: intra-environment task difficulty transfer, cross-environment transfer, and transfer versus forgetting in sequential multi-environment training. Our experiments reveal that while RFT generalizes well within the same environment, its cross-environment transfer capability remains limited. Sequential training effectively enhances downstream performance with minimal forgetting, whereas mixed training achieves a better balance in overall generalization. The study further uncovers the critical influence of semantic priors and interface discrepancies on transfer efficacy.
Technology Category
Application Category
📝 Abstract
Reinforcement fine-tuning (RFT) has shown promise for training LLM agents to perform multi-turn decision-making based on environment feedback. However, most existing evaluations remain largely in-domain: training and testing are conducted in the same environment or even on the same tasks. In real-world deployment, agents may operate in unseen environments with different background knowledge, observation spaces, and action interfaces. To characterize the generalization profile of RFT under such shifts, we conduct a systematic study along three axes: (1) within-environment generalization across task difficulty, (2) cross-environment transfer to unseen environments, and (3) sequential multi-environment training to quantify transfer and forgetting. Our results show that RFT generalizes well across task difficulty within an environment, but exhibits weaker transfer to unseen environments, which correlates with shifts in both semantic priors and observation/action interfaces. In contrast, sequential training yields promising downstream gains with minimal upstream forgetting, and mixture training across environments improves the overall balance. We further provide detailed analyses and deeper insights, and hope our work helps the community develop and deploy generalizable LLM agents.
Problem
Research questions and friction points this paper is trying to address.
reinforcement fine-tuning
generalization
LLM agents
cross-environment transfer
distribution shift
Innovation
Methods, ideas, or system contributions that make the work stand out.
Reinforcement Fine-Tuning
Generalization
LLM Agents
Cross-Environment Transfer
Sequential Training
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