Pistis Technical Report
We introduce the Pistis model family, comprising 27B- and 9B-parameter multimodal large language models built on Qwen3.6 and Qwen3.5, respectively, and developed through a general and scalable post-training framework. The framework first establishes a strong foundation through large-scale multimodal supervised fine-tuning (SFT). Building on this SFT foundation, we propose Interleaved Distillation and Reinforcement Learning (IDRL), a novel post-training paradigm that tightly integrates on-policy distillation and reinforcement learning within a single training loop. By alternating between the two objectives, rather than optimizing either in isolation or combining them in a static joint loss...
| Authors | Heyun Chen, Xiaohan Lan, Jiaxi Li et al. |
|---|---|
| Primary category | cs.AI |
| Published | 23 September 2026 |
| Last updated at source | 23 September 2026 |
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