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%.

The image geolocalization task aims to predict the location where an image was taken anywhere on Earth using visual clues.Existing large vision-language model (LVLM) approaches leverage world knowledge, chain-of-thought reasoning, and agentic capabilities, but overlook a common strategy used by humans — using maps.In this work, we first equip the model Thinking with Map ability and formulate it as an agent-in-the-map loop.We develop a two-stage optimization scheme for it, including agentic reinforcement learning (RL) followed by parallel test-time scaling (TTS).The RL strengthens the agentic capability of model to improve sampling efficiency, and the parallel TTS enables the model to explore multiple candidate paths before making the final prediction, which is crucial for geolocalization.To evaluate our method on up-to-date and in-the-wild images, we further present MAPBench, a comprehensive geolocalization training and evaluation benchmark composed entirely of real-world images.Experimental results show that our method outperforms existing open- and closed-source models on most metrics, specifically improving Acc@500m from 8.0% to 22.1% compared to Gemini-3-Pro with Google Search/Map grounded mode.

Reinforcement learning for LLM agents is typically conducted on a static data distribution, which fails to adapt to the agent’s evolving behavior and leads to poor coverage of complex environment interactions. To address these challenges, we propose CoEvolve, an agent-data mutual evolution framework that enables LLM agents to improve through closed-loop, interaction-driven training. Specifically, CoEvolve extracts feedback signals such as forgetting and uncertainty from rollout trajectories to identify failure-prone interaction patterns, and utilizes them to guide LLM-based task synthesis. The synthesized tasks are validated through environment interaction and utilized to update the data distribution, enabling joint adaptation of the agent and its data. Extensive experiments on AppWorld and BFCL across Qwen2.5-7B, Qwen3-4B, and Qwen3-30B-A3B demonstrate consistent and significant improvements over strong base models, yielding absolute gains of 19.43%, 15.58%, and 18.14%, respectively.