Skill1: Unified Evolution of Skill-Augmented Agents via Reinforcement Learning

Skill1 presents a unified reinforcement learning framework that enables language model agents to co-evolve three coupled capabilities: skill selection, utilization, and distillation from a single task-outcome reward signal. Demonstrated improvements over existing baselines on complex tasks suggest advances in how AI agents can build and leverage persistent skill libraries across diverse problem domains.

Skill1 addresses a fundamental challenge in agent learning: coordinating multiple interdependent capabilities toward coherent improvement. Previous approaches treated skill selection, task execution, and knowledge distillation as separate optimization problems, creating misaligned incentives and suboptimal learning dynamics. This research proposes a unified policy that learns all three capabilities from a single reward signal, using temporal variations in that signal to appropriately credit different learning phases.

The framework reflects a broader trend in AI research toward reducing fragmentation in multi-objective learning. As language models become increasingly deployed as autonomous agents, the ability to accumulate, organize, and reuse knowledge efficiently becomes critical. Skill libraries provide a mechanism for transfer learning across tasks, but maintaining them requires careful orchestration of what skills to invoke, how effectively to apply them, and when to extract generalizable knowledge from new experiences.

The technical innovation lies in the credit assignment mechanism: low-frequency trends in the reward signal guide skill selection improvements while high-frequency variations target distillation refinement. This dual-frequency approach elegantly sidesteps the need for separate auxiliary rewards. Empirical validation on ALFWorld and WebShop—complex interactive environments—demonstrates practical benefits over both skill-based and pure reinforcement learning baselines.

For AI development communities, this work indicates maturing methodologies for agent self-improvement. Success here could influence how conversational AI systems learn from user interactions in production environments, though significant engineering work remains between academic validation and real-world deployment at scale.

• →Skill1 unifies skill selection, utilization, and distillation under a single reinforcement learning objective with temporal credit assignment
• →Low-frequency and high-frequency reward signal variations automatically credit different learning phases without auxiliary rewards
• →Experiments on ALFWorld and WebShop show consistent improvements over prior skill-based and RL baselines
• →Co-evolution of all three capabilities confirmed through training dynamics analysis and ablation studies
• →Framework advances persistent skill library maintenance for language model agents across diverse tasks