We have 3 of 25 papers
We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.
Not the right person? Other researchers publish under this name.
Masked diffusion language models (dLLMs) generate text by iteratively denoising masked tokens with bidirectional attention. Extending reasoning across generation chunks normally requires keeping earlier generated text in context. We ask whether a dLLM can instead continue reasoning after that text is cleared, using onl...
This work proposes a reinforcement learning (RL) fine-tuning framework that treats EHR foundation models as generative policies over patient trajectories, and finds that RL fine-tuning consistently improves over pre-trained backbones and strong baselines.
Experiments on diverse open-source text classification datasets show that AHR can outperform standard baselines like LoRA when learning from limited samples, despite having 200-1000x fewer trainable parameters, as the authors' AHR only modifies ~0.0001% of the model's parameters.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
