# Safe Reinforcement Learning

Clinical multi-turn agents must decide what to ask, when to stop, and what to do from incomplete information. Across three controlled simulated settings, medication refill triage, acute diagnostic work-up and dosing titration, the study compares reward designs: outcome-only GRPO often raises average value by asking fewer questions while producing premature commitment and weaker high-risk performance, whereas extra credit for questions that reduce uncertainty about the hidden state raises held-out value and moderate-case support across three training seeds in diagnostics without the high-risk seed collapse seen under outcome-only training.

Clinical multi-turn agents must decide what to ask, when to stop, and what to do from incomplete information. Across three controlled simulated settings, medication refill triage, acute diagnostic work-up and dosing titration, the study compares reward designs: outcome-only GRPO often raises average value by asking fewer questions while producing premature commitment and weaker high-risk performance, whereas extra credit for questions that reduce uncertainty about the hidden state raises held-out value and moderate-case support across three training seeds in diagnostics without the high-risk seed collapse seen under outcome-only training.

ShieldVLA is a safety-aligned fine-tuning framework for vision-language-action (VLA) models that learns a model-free Hamilton-Jacobi reachability value function from visual observations as a safety critic, uses that critic to gate policy optimization into reward maximization inside feasible regions and recovery near unsafe states, and replaces manual per-step cost labels with rubric-based VLM safety scores; across five navigation and manipulation benchmarks (Dubins-VL, TurtleBot-Nav, Safety-CHORES Nav/Fetch, Franka-Reach) and multiple VLA backbones it reduces cumulative safety cost by 57% on average and improves task success rate by 0.13 over SafeVLA.

ShieldVLA is a safety-aligned fine-tuning framework for vision-language-action (VLA) models that learns a model-free Hamilton-Jacobi reachability value function from visual observations as a safety critic, uses that critic to gate policy optimization into reward maximization inside feasible regions and recovery near unsafe states, and replaces manual per-step cost labels with rubric-based VLM safety scores; across five navigation and manipulation benchmarks (Dubins-VL, TurtleBot-Nav, Safety-CHORES Nav/Fetch, Franka-Reach) and multiple VLA backbones it reduces cumulative safety cost by 57% on average and improves task success rate by 0.13 over SafeVLA.