提出可学习的逐步语言反馈机制STRIDE，让LLM在推理过程中自动修正错误，提升复杂推理任务准确性。
arXiv:2605.18851v1 Announce Type: new Abstract: Recent advances in Reinforcement Learning (RL) have underscored its potential for incentivizing reason…
医学影像生成新挑战：揭示Latent Diffusion模型在医学数据上的可学习性差距，推动更可靠的AI诊断工具发展。
arXiv:2605.17087v1 Announce Type: new Abstract: Generative data augmentation with latent diffusion models is a promising strategy for addressing class…
揭示自我对弈仅在自合成数据提供可学习信息增益时才有效演化，为AI训练策略提供关键理论指导。
arXiv:2603.02218v2 Announce Type: replace Abstract: Large language models (LLMs) make it plausible to build systems that improve through self-evolving…
提出LEAP可学习端到端自适应剪枝方法，在保持大语言模型性能的同时实现高效压缩
arXiv:2605.17289v1 Announce Type: new Abstract: Unstructured sparsity is now natively accelerated by recent GPU kernels and dataflow hardware, shiftin…
揭示RLVR训练中LLM对困难样本无法学习的反直觉现象，挑战现有认知
arXiv:2605.16787v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Reward (RLVR) has proven effective in improving Large Language …
探讨Chain-of-Thought验证器的在线可学习性，深入分析正确性与完备性间的权衡关系。
arXiv:2603.03538v3 Announce Type: replace Abstract: Large Language Models (LLMs) with chain-of-thought generation have demonstrated great potential fo…