Karl28/RSIPO-Qwen2.5-3B-Instruct-Search
AI model by Karl28
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The search-based QA policy trained with RSIPO from Qwen/Qwen2.5-3B-Instruct, reported in Table 3 of the paper: the checkpoint after 150 RL steps, in bf16. Built with Qwen. RSIPO (Recursive Self-Improving Policy Optimization) is a self-evolving agentic RL framework in which agentic skills and the policy co-evolve during training. It builds on SEED's hybrid of RLVR and on-policy self-distillation and adds an execution check: the policy rewrites a skill from one of its rollouts, a skill-enhanced teacher replays the task from the same initial state, and the measured replay gain both rewards the skill (on-policy skill evolution) and sets the weight of the self-distillation loss (trust-calibrated self-distillation). No skill is used at test time. The policy acts with the agent prompts of the RSIPO code. To reproduce…
Read the full model card ↗ · Preview checked 2026-11-01T01:07:28.607Z
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Model overview
Karl28/RSIPO-Qwen2.5-3B-Instruct-Search is a task-unspecified model repository published by Karl28 on Hugging Face. A library was not reported.
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- Created
- 2026-10-11T06:11:09.000Z
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- 2026-10-11T06:11:09.000Z
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