NEWSLETTER
The reinforcement learning of verifiable rewards (RLVR) has recently become a promising method to improve reasoning skills in language models without direct supervision. This approach has shown remarkable success in mathematics and coding, where reasoning is naturally aligned with the structured resolution of problems. While studies have shown that RLVR can only lead to a highway reasoning, research has greatly limited these technical fields. The efforts to extend RLVR have explored synthetic data sets, such as those that involve sequential tasks and object count, which indicate potential but also highlight the challenges of adapting this method to different domains.
The expansion of RLVR to broader areas remains an open challenge, particularly in tasks such as the answer to multiple choice questions (MCQA), which provides structured and verifiable labels in various topics, including medicine. However, unlike mathematics and coding, which involve complex reasoning with an open response space, MCQA tasks generally have predefined response options, which makes RLVR benefits translate effectively. This limitation is especially relevant in medical reasoning tasks, where models must navigate intricate clinical knowledge to produce precise responses, an area that has proven to be difficult for existing ai systems.
Microsoft Research researchers investigate whether medical reasoning can arise through RLVR. They present Med-RLVR, taking advantage of the medical MCQA data to evaluate the effectiveness of RLVR in the medical domain. Their findings show that RLVR extends beyond mathematics and coding, achieving performance comparable to fine adjustment (SFT) supervised in distribution tasks, while significantly improving generalization out of distribution in eight percentage points. Analyzing training dynamics, they observe that reasoning capabilities arise in a 3B parameter base model without explicit supervision, highlighting RLVR's potential to advance reasoning in intensive fields in knowledge such as medicine.
RL optimizes decision -making by training an agent to maximize rewards through interactions with an environment. It has effectively applied to language models to align exits with human preferences and, more recently, to cause reasoning without explicit supervision. This study uses proximal policies optimization (PPO) to train a policy model, incorporating an objective function trimmed to stabilize training. Using a rules-based reward function, Med-RLVR assigns rewards based on the correction of the output and validity of the format. Without additional supervision, the model demonstrates an emerging medical reasoning, similar to mathematical reasoning in previous RLVR studies, highlighting RLVR's potential beyond structured domains.
The Medqa-Use data set, which includes questions about the multiple medical examination, is used to train MED-RLVR. Unlike the standard version of four options, this data set presents a greater challenge by offering more response options. The training is based on the QWEN2.5-3B model that OpenRLHF uses for reinforcement learning. Compared to SFT, MED-RLVR demonstrates a higher generalization, particularly in the MMLU-PRO-SALUD data set. The analysis reveals six stages of evolution of reasoning: format failures, detailed outputs, reward piracy and reimbursement. Unlike mathematical or coding tasks, no self -validation behaviors (“Aha-momments”) were observed, which suggests potential improvements through short reasoning chains or finer adjustment with longer COT.
In conclusion, the study focuses on MCQA in Medicine, providing a controlled environment for evaluation. However, MCQA does not completely capture the complexity of real world's tasks, as an open text response, generation of medical reports or dialogues. In addition, the unimodal approach limits the model of the model to integrate multimodal data, which is crucial for diagnostic applications. Future work must address these limitations. Med-RLVR, based on reinforcement learning with verifiable rewards, coincides with SFT in distribution tasks and improves the generalization out of distribution. While medical reasoning arises without explicit supervision, challenges such as rewards piracy persist, highlighting the need for greater exploration of complex reasoning and multimodal integration.
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Sana Hassan, a consulting intern in Marktechpost and double grade student in Iit Madras, passionate to apply technology and ai to address real world challenges. With great interest in solving practical problems, it provides a new perspective to the intersection of ai and real -life solutions.
