将推理链分解为可验证子问题，用课程强化学习精准分配LLM推理中的信用，突破训练瓶颈。
arXiv:2605.22074v1 Announce Type: cross Abstract: Reinforcement learning from verifiable rewards (RLVR) has shown strong promise for LLM reasoning, bu…
揭示RLVR训练中参数轨迹的秩一结构，仅需极小规模训练即可外推LLM推理能力，颠覆传统认知。
arXiv:2605.21468v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has become a dominant paradigm for improving rea…
评估多模态大模型操作中心链式思维推理能力的新基准，强调接地与可验证性。
arXiv:2605.19559v1 Announce Type: new Abstract: The rapid development of Multimodal Large Language Models (MLLMs) has led to growing interest in egoce…
单64KB超向量承载无限LLM记忆，支持离线可验证与GDPR遗忘，AI代理记忆的突破性方案。
Article URL: https://github.com/OpenAgentic-Labs/echoform-ghost-memory Comments URL: https://news.ycombinator.com/item?id=48192963 Points: 1 # Comment…
探索强化学习与可验证奖励在知识密集型领域对LLM推理能力的提升，填补研究空白。
arXiv:2605.18261v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has demonstrated promising potential to enhance …
提出RLBFF，用二元灵活反馈桥接人类偏好与可验证奖励，提升大模型对齐效率。
arXiv:2509.21319v3 Announce Type: replace-cross Abstract: Reinforcement Learning with Human Feedback (RLHF) and Reinforcement Learning with Verifiable…
提出可验证的LLM上下文压缩形式化框架，压缩同时保持承诺完整性，AI安全新思路。
arXiv:2605.17304v1 Announce Type: new Abstract: LLM context is not just tokens; it is a set of commitments. Long-running conversations accumulate goal…
58位作者、30家机构联合发布报告，提出10种机制来增强AI系统安全、公平、隐私方面的可验证性。
We’ve contributed to a multi-stakeholder report by 58 co-authors at 30 organizations, including the Centre for the Future of Intelligence, Mila, Schwa…
突破现有强化学习局限，提出通过主动交互提升大模型推理能力的新方法。
arXiv:2605.08401v2 Announce Type: replace-cross Abstract: Recent advances in large language models (LLMs) have demonstrated remarkable reasoning capab…
视频扩散模型不再只求逼真，引入强化学习实现时空逻辑约束下的可验证推理，提升智能体规划能力。
arXiv:2605.15458v1 Announce Type: new Abstract: Video diffusion models have made rapid progress in perceptual realism and temporal coherence, but they…
提出Agent技能作为可验证工件，用双条件正确性标准解决人机协作信任问题，LLM部署的新范式
arXiv:2605.00424v2 Announce Type: replace-cross Abstract: Agent skills - structured packages of instructions, scripts, and references that augment a l…
用随机少样本指导提升RLVR在困难问题上的样本效率，大模型训练新思路。
arXiv:2605.15012v1 Announce Type: cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has achieved great success in developing Large…