Pistis introduces 27B- and 9B-parameter multimodal models via new training framework
Researchers released the Pistis model family on arXiv, featuring two multimodal large language models built on Qwen3.6 and Qwen3.5. The 27B-parameter model uses Qwen3.6, while the 9B-parameter model is based on Qwen3.5. Both leverage a novel post-training framework called Interleaved Distillation and Reinforcement Learning (IDRL), which alternates between on-policy distillation and reinforcement…
Key points
- Pistis model family includes 27B-parameter (Qwen3.6-based) and 9B-parameter (Qwen3.5-based) multimodal LLMs
- IDRL framework alternates distillation and reinforcement learning for stronger performance and stability
- Pistis-Agentic variant excels in multimodal search and supports long-horizon agentic tasks
The framework produces two specialized variants per scale: Pistis-Thinking for deep multimodal reasoning and Pistis-Agentic for long-horizon planning, iterative reasoning, and tool use. Pistis-Agentic excels in multimodal search. The team also introduced Pistis-Auto-Harnessing (PAH), a system-level method that optimizes inference harnesses without altering model parameters or increasing interaction costs. Experiments show PAH improves performance without parameter updates.
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