GEAR 打破两阶段训练，用端到端自回归协同优化图像生成，让 tokenizer 与生成器无缝配合，产出的图像更真实。
arXiv:2606.32039v1 Announce Type: new Abstract: Visual generative models are typically trained in two stages. A tokenizer is first trained for reconst…
提出联合训练策略与世界观模型的新方法，让语言智能体在复杂任务中表现更佳。
arXiv:2606.02388v1 Announce Type: new Abstract: Reinforcement learning (RL) improves large language model (LLM) agents by teaching them which actions …
打破两阶段训练范式，提出SFT与RL协同强化大模型推理能力的新方法。
arXiv:2509.06948v3 Announce Type: replace Abstract: Supervised fine-tuning (SFT) and reinforcement learning with verifiable rewards (RLVR) are two wid…
用协同训练改写器和密集编码器，跨越自然语言与API技术词汇的鸿沟，大幅提升LLM工具检索准确率。
arXiv:2605.29271v1 Announce Type: new Abstract: Tool retrieval over large API catalogs is a core bottleneck for LLM agents: user queries arrive in col…