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cs.LG2026
No Task Vector Is an Island: A Comprehensive Study on the Composability of Task Vectors from On-Policy Distillation
Jingang Zhou, Feiyu Han, Han Zhu +6
Task vectors provide a simple mechanism for composing learned capabilities through model merging. However, the composability of task vectors produced by on-policy distillation (OPD…
cs.LG2026
Teacher Should Think Ahead: Adaptive Continuations for Reliable On-Policy Distillation
Jingang Zhou, Yuyi Zhou, Haiyang Guo +6
On-policy distillation (OPD) is a promising approach for transferring knowledge between language models, where a student receives dense token-level supervision along its own genera…
cs.LG2025
M2-omni: Advancing Omni-MLLM for Comprehensive Modality Support with Competitive Performance
Qingpei Guo, Kaiyou Song, Zipeng Feng +9
We present M2-omni, a cutting-edge, open-source omni-MLLM that achieves competitive performance to GPT-4o. M2-omni employs a unified multimodal sequence modeling framework, which e…