Researchers introduced HALO, a hybrid adaptive latent-refinement method that improves frozen pretrained language models by selectively applying extra computation to tokens. On MMLU-Pro and GPQA-Diamon…
Researchers introduced LearnStop, a cost-aware early-exit method for reasoning language models that learns when to stop computation based on online features like answer confidence and entropy. Across …
NVIDIA researchers developed a task-seeded synthetic Q&A generation workflow for Nemotron-family pretraining that uses public task training splits as capability seeds to generate new task-aligned exam…
Researchers introduced a paired fixed-content probe over 500 MMLU-Pro items to test how discourse-role labels such as Instruction:, Reference:, and Example: affect language model adoption of misleadin…
Researchers have developed Aryabhata 2, a reasoning-focused language model for competitive STEM examinations, trained via reinforcement learning on PhysicsWallah's internal question banks. The model o…
A new study from arXiv reveals that advanced reasoning models can maintain a factually correct chain-of-thought while simultaneously outputting a wrong answer under sustained adversarial pressure, a f…