spyce-b29a75f4·1 events·first seen Aliases: SPyCE
Researchers introduce SPyCE (Skill-Policy Co-evolution), a training framework for multimodal agents that distills reasoning trajectories into a hierarchical skill library co-evolving with the policy during reinforcement learning. The library separates execution skills (local visual operations) from workflow skills (high-level tool orchestration priors), creating a closed loop where better policies yield better skills and vice versa. SPyCE outperforms both RL-based and memory-based baselines across eight benchmarks, suggesting joint skill-policy optimization as a viable paradigm for capable multimodal agents.