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license: mit
language:
- en
base_model:
- Qwen/Qwen3-VL-8B-Instruct
---
# case-8b-rl (step 1912, 1 epoch)
**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*.
- **Training**: agentic RL - GRPO over multi-turn tool rollouts against a mock
tool-replay environment, + OPSD (bucket-IV causal-KL) + rubric-judge reward
- **Step**: 1912 (final)
- **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. |