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SkillNEO 提出非精英进化优化方法来改进 LLM agent 技能，通过允许低奖励版本继续迭代并分析版本间差异来保留有效步骤、修复失败，使 GPT-5.4 Mini 在 86 项 SkillsBench 任务上平均奖励从 49.1% 提升至 66.4%，且改进后的技能还能提升更强模型的表现。
EmbodiedEvo 让编码代理在模拟机器人场景中编写 Python 代码、执行任务并将经验固化为可复用的技能，从而零样本迁移到真实机器人与新环境，无需更新模型权重。
提出了 SkillGrain 框架，将用户反馈分解为可独立执行的原子规则，预测其可迁移性并只保留适用于未来请求的部分，通过强化学习联合优化技能归纳与条件生成，以实现可复用的个性化技能学习。
该论文提出 Socratic Skill Learning (SSL)，通过将专家教学与学生实践交错进行，使 LLM 代理获得可迁移的可复用技能，并构建了 SkillTransferBench 基准测试，结果显示其在效用指标上优于轨迹蒸馏与对比蒸馏方法。
UC Berkeley 与 Princeton 研究者提出一种可扩展的 empowerment（赋能）最大化配方，将赋能与可执行技能数量联系起来，并在 Crafter 等复杂环境中展示出建房、资源收集等涌现行为。
提出了RSIPO框架，通过技能与策略的协同演化实现智能体强化学习的递归自我优化，在ALFWorld上以Qwen2.5-7B达到98.6%的成功率，显著超越GRPO等基线。
ViSkill 提出一种视觉原生的技能学习框架，将成功交互编码为可复用的视觉技能卡，与策略优化形成闭环反馈，在 Sokoban、FrozenLake 等基准上超越多种基线。
UniSkill 提出一种共享策略与技能库共同演化的框架，通过对比动作反馈作为技能提案的 actor-alignment 信号，避免昂贵的额外 rollout，在 ALFWorld 达到 98.4% 成功率、WebShop 达到 84.7%。
Skill2Env is a capability-oriented framework that synthesizes executable environments from skills to train general AI agents, using task blueprints and iterative hardening to improve agent performance across benchmarks.
Skild AI demonstrated a robot trained to play football using self-play simulation equivalent to 140 years of practice in weeks, illustrating a new approach to skill acquisition through computer simulation.
EvoSkill v2 enables AI agents to self-improve without retraining by using persistent skills stored in files, with a coach agent writing lessons from failed tasks, leading to significant performance gains in tasks like spreadsheet repair.
GeoSkill introduces a hierarchical skill learning framework for geospatial agents to distill execution experience into reusable skills, enhancing task accuracy and tool reliability through collaborative revision.
SPACE 通过从成功轨迹归纳两层参数化技能，将子技能边界作为动作块监督，训练长程 LLM Agent 自适应输出可变长原子动作序列；在 ALFWorld 和 ScienceWorld 上成功率提升 7.0%–31.3%，决策轮次最多降低 78.9%。
Introduces QDOS, a unified pipeline for offline-to-online reinforcement learning that uses advantage-weighted quality-diversity pretraining to extract diverse and high-value skills, significantly improving performance in manipulation and locomotion tasks.
This article presents a method to extract ideas from bloggers' content using AI tools and develop reusable skill workflows.
SAGE is a framework for automating storyboard generation in short drama production using self-evolving rules and attribution-guided updates, achieving expert-level performance and reducing authoring time in commercial deployment.
Introduces ERSkill, a retrieval-centric framework for self-evolving, skill-guided adaptive memory access in LLM agents. It co-evolves retrieval skills and a routing policy, substantially outperforming strong baselines across agent memory benchmarks.
This paper argues that representing agent skills as code, rather than natural language, yields the best cost reduction for LLM agents, and introduces SpeedRunner, a coding agent that learns programmatic skills from past trajectories to improve performance while cutting costs across embodied environments.
Introduces ContinualSkillBench, a dynamic evaluation framework for in-context continual skill learning in LLM agents, showing that while sequential execution improves performance, current methods struggle to consolidate experience into robust, transferable skills.
This paper introduces SkillJack, the first attack targeting the experience-to-skill pipeline of self-evolving agents, showing that poisoned experiences can be transformed into persistent malicious skills that evade detection and survive deletion of original records.