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license: mit
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base_model:
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- Qwen/Qwen3-VL-8B-Instruct
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---
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Paper: *From Given to Gathered Evidence: Agentic Learning for Longitudinal Medical Reasoning*.
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- **Training**:
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tool
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- **Step**: 1912 (final
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- **Provenance**: `case/rl_qwen3vl_tool/hf_step1912` (merged from FSDP shards)
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- **Base model**: Qwen3-VL-8B-Instruct
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- **Task**: longitudinal medical-imaging QA; the policy answers by calling tools
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(view image, compare visits, organ segmentation, report retrieval, skill memory)
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- **Format**: merged HF checkpoint (single `model.safetensors` + tokenizer/processor)
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- **
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- **Data**: trained on UK Biobank-derived, access-controlled data; no UKB-derived
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data is redistributed here. Research use only.
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# case-8b-rl (step 1912, 1 epoch)
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**Clinical Agents for Seeking Evidence** - agentic harness learning for
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longitudinal medical imaging. The model autonomously plans and calls tools inside a
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multi-turn harness (model -> `qwen3_xml` tool call -> environment executes ->
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observation -> ... -> final answer); it never answers single-shot. Three role
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personas: **Anatomist** (organ segmentation / volumetry), **Radiologist**
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(cross-visit image comparison), **Consultant** (report-grounded synthesis).
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Paper: *From Given to Gathered Evidence: Agentic Learning for Longitudinal Medical Reasoning*.
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- **Training**: agentic RL - GRPO over multi-turn tool rollouts against a mock
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tool-replay environment, + OPSD (bucket-IV causal-KL) + rubric-judge reward
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- **Step**: 1912 (final)
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- **Base model**: Qwen3-VL-8B-Instruct
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- **Format**: merged HF checkpoint (single `model.safetensors` + tokenizer/processor)
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- **Serving tip**: run with a tool backend (the released mock-tool replay configs,
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or the full LangGraph agents) and start vLLM with
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`--enable-auto-tool-choice --tool-call-parser qwen3_xml`
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(add `--enforce-eager` when the environment has no full CUDA toolkit)
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- **Data**: trained on UK Biobank-derived, access-controlled data; no UKB-derived
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data is redistributed here. Research use only.
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