MAD-OPD: Breaking the Ceiling in On-Policy Distillation via Multi-Agent Debate
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| Autores principales: | , , , , , , , , , |
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| Formato: | Preprint |
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2026
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| _version_ | 1866913083529625600 |
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| author | Wang, Jianze Liu, Ying Chen, Jinlong Hu, Xuchun Zhang, Qilong Cao, Yu Wang, Jun Yang, Hua Xie, Yong Chen, Qianglong |
| author_facet | Wang, Jianze Liu, Ying Chen, Jinlong Hu, Xuchun Zhang, Qilong Cao, Yu Wang, Jun Yang, Hua Xie, Yong Chen, Qianglong |
| contents | 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 each teacher's contribution weighted by its post-debate confidence. To extend OPD to agentic tasks, we also introduce On-Policy Agentic Distillation (OPAD), which adds step-level sampling to stabilize training under multi-step error compounding. We additionally derive a task-adaptive divergence principle, selecting JSD (Jensen-Shannon divergence) for agentic stability and reverse KL (Kullback-Leibler) divergence for code generation, and verify it both theoretically and empirically. Across six teacher-student configurations (Qwen3 and Qwen3.5; 1.7B-14B students, 8B-32B teachers) and five agentic and code benchmarks, MAD-OPD ranks first across all six configurations; on the 14B+8B$\to$4B setting it lifts the agentic average by $+2.4\%$ and the code average by $+3.7\%$ over the stronger single-teacher OPD. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_01347 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | MAD-OPD: Breaking the Ceiling in On-Policy Distillation via Multi-Agent Debate Wang, Jianze Liu, Ying Chen, Jinlong Hu, Xuchun Zhang, Qilong Cao, Yu Wang, Jun Yang, Hua Xie, Yong Chen, Qianglong Computation and Language Artificial Intelligence Machine Learning 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 each teacher's contribution weighted by its post-debate confidence. To extend OPD to agentic tasks, we also introduce On-Policy Agentic Distillation (OPAD), which adds step-level sampling to stabilize training under multi-step error compounding. We additionally derive a task-adaptive divergence principle, selecting JSD (Jensen-Shannon divergence) for agentic stability and reverse KL (Kullback-Leibler) divergence for code generation, and verify it both theoretically and empirically. Across six teacher-student configurations (Qwen3 and Qwen3.5; 1.7B-14B students, 8B-32B teachers) and five agentic and code benchmarks, MAD-OPD ranks first across all six configurations; on the 14B+8B$\to$4B setting it lifts the agentic average by $+2.4\%$ and the code average by $+3.7\%$ over the stronger single-teacher OPD. |
| title | MAD-OPD: Breaking the Ceiling in On-Policy Distillation via Multi-Agent Debate |
| topic | Computation and Language Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2605.01347 |