aiqbench analysis
Role-Agent advances LLM agent training by enabling a single model to simultaneously act as both the environment and the learner, effectively bootstrapping co-evolution without external simulators. This framework improves generalization on complex tasks through process rewards for state prediction and dynamic reshaping of training data based on failure analysis. The approach offers a promising path toward more efficient agent development by reducing reliance on static or costly interaction feedback loops.
Role-Agent: Bootstrapping LLM Agents via Dual-Role Evolution
arXiv:2606.10917v2 Announce Type: replace Abstract: Although Large Language Model (LLM) agents have demonstrated strong performance on complex tasks, their learning is often limited by inefficient interaction feedback and static training environments, which hinder broader generalization. To address these limitations, this paper introduces Role-Agent, \textcolor{black}{a framework} that harnesses a single LLM to function concurrently as both the agent and the environment, enabling a bootstrapped co-evolution. Role-Agent comprises two synergistic components: World-In-Agent (WIA) and Agent-In-World (AIW). In WIA, the LLM acts as the agent and predicts future states after each action; the alignment between predicted and actual states is then used as a process reward, encouraging environment-aware reasoning. In AIW, the LLM analyzes failure modes from failed trajectories and retrieves tasks with similar failure patterns, thereby reshaping the training data distribution for targeted practice. Experiments on multiple benchmarks show that Role-Agent consistently improves performance, yielding an average gain of over 4\% over strong baselines.
Abstract:Although Large Language Model (LLM) agents have demonstrated strong performance on complex tasks, their learning is often limited by inefficient interaction feedback and static training environments, which hinder broader generalization. To address these limitations, this paper introduces Role-Agent, \textcolor{black}{a framework} that harnesses a single LLM to function concurrently as both the agent and the environment, enabling a bootstrapped co-evolution. Role-Agent comprises two synergistic components: World-In-Agent (WIA) and Agent-In-World (AIW). In WIA, the LLM acts as the agent and predicts future states after each action; the alignment between predicted and actual states is then used as a process reward, encouraging environment-aware reasoning. In AIW, the LLM analyzes failure modes from failed trajectories and retrieves tasks with similar failure patterns, thereby reshaping the training data distribution for targeted practice. Experiments on multiple benchmarks show that Role-Agent consistently improves performance, yielding an average gain of over 4\% over strong baselines.
Artificial Intelligence (cs.AI)
arXiv:2606.10917 [cs.AI]
(or arXiv:2606.10917v2 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.10917 Focus to learn more arXiv-issued DOI via DataCite
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