Papers arxiv:2605.02943 Healthcare AI GYM for Medical Agents Published on May 1 Submitted by Minbyul Jeong on May 6 Authors: Abstract Multi-turn clinical reasoning training using reinforcement learning reveals…
…achieve commercial-quality output for Indic languages without requiring new acoustic decoders or commercial training data. AI-generated summary Commercial TTS systems produce near-native Indic audio, but the best open-source…
…AI-generated summary Large language models (LLMs) are trained for downstream tasks by updating their parameters (e.g., via RL). However, updating parameters forces them to absorb task-specific information, which can…
…But the researchers from Sionic AI already do most of their work with Claude Code. It writes training scripts, debugs CUDA errors, searches hyperparameters overnight. For the actual work of building models…
…training and enabling asynchronous generation across data modalities. AI-generated summary In this paper, we study an under-explored but important factor of diffusion generative models , i.e., the combinatorial complexity . Data…
Papers arxiv:2605.22771 Reducing Political Manipulation with Consistency Training Published on May 28 Submitted by Long Phan on May 29 Center for AI Safety Authors: , , , , , Abstract Large language models demonstrate systematic…
…it scales effectively with both data and model size; and (3) Performance: it achieves competitive or superior results across diverse multimodal benchmarks . Trained on 8B samples from Recap-DataComp-1B , GenLIP matches…
…AI-generated summary Continual post-training aims to extend large language models (LLMs) with new knowledge, skills, and behaviors, yet it remains unclear when sequential updates enable capability transfer and when they…
…Moreover, an expressive performance rendering model trained on PianoCoRe demonstrates improved robustness to unseen pieces compared to models trained on raw or smaller datasets. PianoCoRe provides a ready-to-use foundation for…
…To disentangle data effects, we consider two regimes across different dataset size s: (1) concept generalization , where each individual concept is observed during training under potentially imbalanced data distributions, and (2) compositional…
