MAD-OPD: Breaking the Ceiling in On-Policy Distillation via Multi-Agent Debate
On-policy distillation (OPD) trains a student on its own trajectories under token-level teacher supervision, but existing methods are capped by a single-teacher capability ceiling: when the teacher errs, the student inherits the error. OPD also remains largely unexplored in agentic tasks, where per-step errors compound across long trajectories and destabilize training. We propose MAD-OPD (Multi-Agent Debate-driven On-Policy Distillation), which breaks this ceiling by recasting the distillation teacher as a deliberative collective of teachers that debate over the student's on-policy state; the debate produces an emergent collective intelligence that supplies token-level supervision, with e...
| Authors | Jianze Wang, Ying Liu, Jinlong Chen et al. |
|---|---|
| Primary category | cs.CL |
| Published | 2 May 2026 |
| Last updated at source | 2 May 2026 |
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