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- ---
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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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- # case-8b-rl (step 1912,1 epoch)
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-
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- **Final RL checkpoint** of CASE - a multi-turn tool-use RL agent for medical imaging.
 
 
 
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  Paper: *From Given to Gathered Evidence: Agentic Learning for Longitudinal Medical Reasoning*.
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- - **Training**: GRPO + OPSD (bucket-IV causal-KL) + rubric-judge reward; multi-turn
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- tool rollout over three clinician roles (Anatomist / Radiologist / Consultant)
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- - **Step**: 1912 (final; run `grpo_qwen3vl_tool_rl`)
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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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- - **Usage**: `vllm serve <this_dir> --trust-remote-code` (add `--enforce-eager` when
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- serving in an environment without a 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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+ # 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.