Records found: 4
'Uni-MoE-2.0-Omni is an open omnimodal MoE on a Qwen2.5-7B core that unifies text, image, audio and video via a shared token interface, dynamic expert routing and staged training for robust multimodal understanding and generation.'
'OpenAI research shows hallucinations are statistical consequences of pretraining and that binary benchmarking incentivizes overconfident guessing; changing evaluation to reward calibrated uncertainty can reduce hallucinations.'
Meta and NYU developed a semi-online reinforcement learning method that balances offline and online training to enhance large language model alignment, boosting performance in both instruction-based and mathematical tasks.
Researchers introduce Regularized Policy Gradient (RPG), a novel framework leveraging KL divergence in off-policy reinforcement learning to significantly improve reasoning and training stability in large language models.