Large language model (LLM) agents are trained with reinforcement learning (RL) for complex decision-making tasks. However, most RL-trained agents remain episodic and cannot accumulate reusable knowledge across episodes. Recent skill-based approaches, such as SkillRL, attempt to address this issue by extracting skills from raw trajectories, but treat the skill bank as an append-only repository without verifying whether stored skills remain effective. In this paper, we propose SkillForge, a framework for continuous skill evolution that enables skills to be verified and refined through environment interaction. By making skill usage explicit during agent interaction, RL can directly optimize both environment actions and skill invocation decisions. SkillForge further introduces evidence-based skill verification and multi-pathway skill induction, allowing the skill bank to continuously grow while maintaining its quality. Extensive experiments on ALFWorld, WebShop, and AppWorld show that SkillForge consistently outperforms SkillRL, demonstrating the effectiveness of continuously verified skills in training stronger LLM agents.

Although Large Language Model (LLM) agents have demonstrated remarkable performance on complex, real-world tasks, the significant bottleneck of execution inefficiencies and static environmental feedback hinders agents’ effective generalization to open-ended scenarios. To address these limitations, this paper introduces Role-Agent, a novel framework that repurposes one LLM to concurrently function as the agent and the environment, thereby enabling a bootstrapped co-evolution. At the core of Role-Agent are two synergistic components: World-In-Agent (WIA) and Agent-In-World (AIW). In WIA where the LLM acts as the agent, we define the reward for an action as the alignment between the agent’s prediction of the next several states and the actual state. These rewards are subsequently grouped by state and optimized, equipping the agent with predictive reasoning capabilities regarding the environment. In AIW where the LLM provides environmental adjustments, we require it to analyze the typical failure modes from failed trajectories, then searches for tasks that exhibit similar failure modes, thereby altering the data distribution to facilitate learning. Extensive experiments conducted on the various benchmarks demonstrate the substantial performance improvements achieved by Role-Agent, yielding an average performance gain of over 4%.