REVIEW 7 cited by
Med-RLVR: Emerging Medical Reasoning from a 3B base model via reinforcement Learning
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Reinforcement learning from verifiable rewards (RLVR) has recently gained attention for its ability to elicit self-evolved reasoning capabilitie from base language models without explicit reasoning supervisions, as demonstrated by DeepSeek-R1. While prior work on RLVR has primarily focused on mathematical and coding domains, its applicability to other tasks and domains remains unexplored. In this work, we investigate whether medical reasoning can emerge from RLVR. We introduce Med-RLVR as an initial study of RLVR in the medical domain leveraging medical multiple-choice question answering (MCQA) data as verifiable labels. Our results demonstrate that RLVR is not only effective for math and coding but also extends successfully to medical question answering. Notably, Med-RLVR achieves performance comparable to traditional supervised fine-tuning (SFT) on in-distribution tasks while significantly improving out-of-distribution generalization, with an 8-point accuracy gain. Further analysis of training dynamics reveals that, with no explicit reasoning supervision, reasoning emerges from the 3B-parameter base model. These findings underscore the potential of RLVR in domains beyond math and coding, opening new avenues for its application in knowledge-intensive fields such as medicine.
Forward citations
Cited by 7 Pith papers
-
MedUPS: Towards Diagnostic Assistance in Uncommon Medical Cases with Large Language Models
Mid-stream alignment on next-step clinical decisions (MedUPS) improves LLM next-step accuracy on uncommon cases, but the size of the gain depends substantially on which LLM judge does the scoring.
-
Can Large Language Models Develop Strategic Reasoning? Post-training Insights from Learning Chess
Training LLMs with dense chess rewards improves puzzle accuracy but plateaus around 25-30%, far below expert level, because the base models lack internal chess knowledge.
-
Med-U1: Incentivizing Unified Medical Reasoning in LLMs via Large-scale Reinforcement Learning
A pure reinforcement learning recipe with mixed rule-based rewards and length control improves Qwen2.5-based models across diverse medical QA formats.
-
Reasoning Like an Economist: Post-Training on Economic Problems Induces Strategic Generalization in LLMs
Post-training an LLM on economic reasoning problems improves accuracy on economic benchmarks and, without game-specific training, raises its Nash equilibrium frequency and win rates in strategic games.
-
Training LLMs for EHR-Based Reasoning Tasks via Reinforcement Learning
A 3B model trained with a small SFT warm-up followed by verifiable-reward RL matches or exceeds far larger models on EHR-based medical calculation, trial matching, and diagnosis tasks.
-
Reasoning Before Translation: Enhancing Legal Machine Translation with Structured Reasoning
On Swiss legal translation, reinforcement learning with a ChrF reward improves small open models more than supervised fine-tuning, but frontier reasoning models still score higher.
-
Trading Human Curation for Synthetic Augmentation in RLVR
Gated synthetic augmentations of a 10-task human base substitute for ~87 extra human RLVR tasks on aggregate held-out pass@1, with cost-adjusted trade rate ρ_cost in [1.4×, 11.6×].
Discussion (0). Continue with ORCID to comment.