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| license: mit |
| --- |
| # case-8b-rsft |
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|
| **Clinical Agents for Seeking Evidence** - agentic harness learning for |
| longitudinal medical imaging. The model autonomously plans and calls tools inside a |
| multi-turn harness (model -> `qwen3_xml` tool call -> environment executes -> |
| observation -> ... -> final answer); it never answers single-shot. Three role |
| personas: **Anatomist** (organ segmentation / volumetry), **Radiologist** |
| (cross-visit image comparison), **Consultant** (report-grounded synthesis). |
| Paper: *From Given to Gathered Evidence: Agentic Learning for Longitudinal Medical Reasoning*. |
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| SFT-initialized agent (40% of the training split) - already tool-calling capable; |
| this is the initialization that the RL models continue from. |
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| - **Training**: mixed single-turn SFT over the three agent roles, assistant-only loss |
| - **Step**: final = 1515（1 epoch） |
| - **Base model**: Qwen3-VL-8B-Instruct |
| - **Format**: merged HF checkpoint (single `model.safetensors` + tokenizer/processor) |
| - **Serving tip**: run with a tool backend (the released mock-tool replay configs, |
| or the full LangGraph agents) and start vLLM with |
| `--enable-auto-tool-choice --tool-call-parser qwen3_xml` |
| (add `--enforce-eager` when the environment has no full CUDA toolkit) |
| - **Data**: trained on UK Biobank-derived, access-controlled data; no UKB-derived |
| data is redistributed here. Research use only. |
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