Key Info

Hugging Face researchers tested multi-harness reinforcement learning, training a model across multiple agent interfaces instead of just one, to address the problem that models degrade when moved between harnesses.

Highlights

  • Problem: a model trained in one harness often loses accuracy or emits invalid tool calls when moved to another
  • Root cause: single-harness training teaches model-specific tool names, output formats, and control flow rather than general task-solving
  • Approach: multi-harness RL trains through several agent interfaces such as Claude Code, Codex, and OpenCode
  • Goal: produce a portable open-weight model that generalizes across different harnesses