Mosaic: Multi-Session Reinforcement Learning for Agentic Trajectories

Mosaic addresses the mismatch between single-session training assumptions and multi-session inference execution caused by context compaction or subagent forking. It introduces a framework to support arbitrary session numbers within a single trajectory, moving beyond broadcasting trajectory-level advantages to better distinguish individual session contributions.