CDPR: Counterfactual Advantage-based Credit Assignment for Cost-Aware Sequential Medical Diagnosis

Researchers propose CDPR (Counterfactual Diagnostic Process Reward), a reinforcement learning method for cost-aware sequential medical diagnosis that assigns credit by comparing chosen actions against alternatives via short rollouts, eliminating the need for expert labels or a learned critic. Integrated into GRPO and tested on MIMIC-IV, ClinicalBench, and a private hospital dataset, CDPR improves diagnostic accuracy while reducing the number and cost of examinations.

arXiv:2608.28599v1 Announce Type: new Abstract: Clinical diagnosis is a step-by-step, cost-aware process: a physician orders examinations one at a time, observes the results, and updates the diagnosis before reaching a final conclusion. Most medical language models instead treat diagnosis as a one-pass classification task and ignore the trade-off between a test's value and its cost. We model diagnosis as a cost-aware sequential decision process and train the policy with reinforcement learning. The main difficulty is credit assignment: the only reliable signal comes once at the end of a long trajectory, so it scores a wasteful workup the same as an efficient one. We propose CDPR Counterfactual Diagnostic Process Reward , which needs no expert labels and no learned critic. CDPR first finds the states where the policy hesitates, using the uncertainty of its action distribution, and then scores the chosen action by its advantage over the alternatives the policy itself would consider, estimated with short rollouts under a utility that balances correctness against test count, cost, and infeasible requests. A rollout cache reuses within-batch trajectories to keep the cost low. We integrate CDPR into GRPO and test it on one in-domain MIMIC-IV and two out-of-domain ClinicalBench and a private hospital dataset benchmarks. CDPR improves diagnostic accuracy while clearly reducing the number and cost of examinations.