Reinforcement learning

最近完整七日的证据

来源提及

8

前七日 10

不同来源

2

前期 2

可比较增长

不可比较

榜单记录

0

采集观测与无发布日期的记录，和有日期的原文提及分别统计。 七日观测：6 · 无日期记录：0

每日趋势 · 最多 90 天

柱形表示有日期的来源提及。最新 UTC 日可能尚未结束。零提及和缺少采集覆盖是不同情况。

虚线标记表示当日无记录，不作为零提及统计。

来源证据

当前展示证据的来源：V2EX 1 · HF Daily Papers 3 · arXiv 15 · TechCrunch 1 · MIT News 1 · Hacker News 2

1. V2EX · 原文发布 2026年11月4日

   [分享创造] 做了一个逆向 Agent，专门解决无人值守和高难样本

   最近做了个有点不一样的逆向工程 Agent 。想解决的其实是个很实际的问题：逆向任务一复杂，AI 很容易跑偏，或者分析到一半就说自己搞定了。目前基于强化学习牵引所以目前支持 WEB 、PE 、ELF 、APK 、固件等方向，可以一边学习一边分析，也会把做过的方法和结果沉淀下来，后续任务能继续积累经验。项目还在迭代，欢迎对 AI 逆向感兴趣的朋友试试，也欢迎直接拿真实样本来交流和 star 。GitHub： https://github.com/amd2g2zz/kunglao-agent

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2. HF Daily Papers · 原文发布 2026年11月4日

   What Does Multi-Turn Reinforcement Learning Really Improve in LLM Agents?

   While multi-turn agentic reinforcement learning (RL) excels on long-horizon tasks, its performance gains remain highly sensitive to interaction constraints. Evaluating across sampling breadth K and interaction depth T via Pass@(K,T), we show that trajectory-level RL advantages depend on sufficient turn budgets, diminishing significantly when budgets tighten.

3. arXiv · 原文发布 2026年11月3日

   OmniPiano: Diverse Dexterous Piano-Playing Challenges for Standard, Robust, Safe, and Multi-Agent RL

   arXiv:2611.00243v1 Announce Type: new Abstract: Dexterous robotic manipulation remains a major challenge in robotics, exemplified by piano playing, which requires coordinated control of fingers and provides a demanding testbed for reinforcement learning (RL). Although previous work established a benchmark for robotic piano playing, it is restricted to two ha

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4. arXiv · 原文发布 2026年11月2日

   Li$^{2}$SAO: Lifting Light Singl-Rollout Asynchronous RL Optimization via Group-Relative Actor-Critic Bootstrapping

   arXiv:2610.40726v1 Announce Type: new Abstract: Asynchronous reinforcement learning (RL) is becoming a mainstream paradigm for post-training large language models (LLMs), as it improves training efficiency by updating the model as rollouts arrive. Recent work argues that group-wise sampling in GRPO does not naturally fit asynchronous RL training because roll

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5. arXiv · 原文发布 2026年11月2日

   CoFiBreak: A Coarse-to-Fine Diversity Reward for Effective Multi-Turn Jailbreak

   arXiv:2610.40926v1 Announce Type: new Abstract: Automated red teaming trains an attacker large language model (LLM) to elicit harmful responses from a victim LLM, but reinforcement learning can collapse attackers onto a narrow set of prompts when attack success is the only reward. Such a collapse of attack diversity can bias safety evaluation toward repeated

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6. HF Daily Papers · 原文发布 2026年11月2日

   Dory1.0-4B: A Looped Normalized-GPT

   We introduce Dory1.0-4B, a 4-billion-parameter looped (latent-recurrent) language model with elastic depth: a single checkpoint serves different inference compute budgets. Dory1.0-4B is built on a normalized Transformer (nGPT) backbone with interleaved sliding-window and full-attention layers, organized into an input block, a shared recurrent block, and an o

7. arXiv · 原文发布 2026年10月31日

   GARDEN: Generalist Autonomous Reward Design Through Evidence Navigation for Cooperative Multi-Agent Reinforcement Learning

   arXiv:2610.37926v1 Announce Type: new Abstract: Cooperative multi-agent reinforcement learning (MARL) enables teams of agents, such as robots and autonomous vehicles, to learn coordinated behavior, but what such teams learn is largely determined by their reward function. Because task success alone provides too little guidance for learning, intermediate rewar

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8. arXiv · 原文发布 2026年10月31日

   AI-Driven Digital Twin Framework for Warehouse Logistics Automation Optimization

   arXiv:2610.38101v1 Announce Type: new Abstract: In recent years, warehouse logistics optimization has increasingly focused on intelligent, data-driven systems to enhance operational efficiency. Conventional static product placement strategies are being progressively replaced by dynamic slotting approaches that can adapt to real-time variations in warehouse c

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9. arXiv · 原文发布 2026年10月28日

   CONCUR: Concordant Auxiliary Rewards for Multi-Task, Multi-Reward Reinforcement Learning

   arXiv:2610.36437v1 Announce Type: new Abstract: When a Large Language Model (LLM) is trained with Reinforcement Learning (RL) on multiple tasks, a task's primary reward can gate auxiliary rewards such as a length reward: on a failing response, an auxiliary cannot exceed the primary. However, group-relative advantage estimators treat a gated auxiliary like an

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10. TechCrunch · 原文发布 2026年10月27日

    Meta AI director Rohit Patel leaves to take on Microsoft Office and Google Workspace

    Rohit Patel is departing Meta after eight years working on AI evaluations and reinforcement learning, TechCrunch has exclusively learned. His startup, Ndoc, aims to take on Microsoft Office Suite.

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11. arXiv · 原文发布 2026年10月27日

    PPO vs. SAC: A Comparative Study for Cooperative Motion Control of Dual-Arm Space Robots

    arXiv:2610.32034v1 Announce Type: new Abstract: The growing population of orbital debris has intensified interest in active debris removal and on-orbit servicing, for which dual-arm space manipulators offer improved grasping stability and load distribution relative to single-arm systems. However, coordinating two manipulators under inter-arm collision avoida

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12. HF Daily Papers · 原文发布 2026年10月26日

    When Fine-Grained Credit Assignment Helps and Hurts: Multimodal Agentic Reinforcement Learning through Video Editing

    Reinforcement learning (RL) has shown promise for training multimodal agents to reason and use tools. However, it remains unclear when fine-grained credit assignment improves learning and when it fails to improve task performance. Video editing provides a useful testbed for this question, combining multimodal perception, long-horizon planning, and tool use w

13. arXiv · 原文发布 2026年10月24日

    Consistency Is Not Validity: Self-Rewarded Moral Argumentation Makes Language Models Decisive but Pliable

    arXiv:2610.30349v1 Announce Type: new Abstract: LLMs are increasingly consulted for moral judgment. We aim to improve performance on such tasks by rewarding good arguments, on the premise that arguing well increases the likelihood of judging well. We train a reinforcement learning policy initialized from a state-of-the-art language model in a self-judged tou

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14. arXiv · 原文发布 2026年10月24日

    Self-Assessment Reinforcement Learning for Search-State Awareness in Retrieval-Augmented Large Language Models

    arXiv:2610.30435v1 Announce Type: new Abstract: Recent advances in search agents have enabled large language models to tackle complex tasks by retrieving and synthesizing external information. However, existing search agents often lack search-state awareness, i.e., the ability to recognize what information has been obtained and what remains missing, leading

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15. arXiv · 原文发布 2026年10月23日

    SpecMAGIC: Specification-Guided Multi-Task Generalization in Multi-Agent Reinforcement Learning

    arXiv:2610.30443v1 Announce Type: new Abstract: Multi-task generalization is an important yet underexplored problem in multi-agent reinforcement learning (MARL). Many real-world applications require multi-agent systems to generalize across complex, temporally extended tasks with diverse inter-agent coordination requirements. We introduce a specification lang

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16. arXiv · 原文发布 2026年10月23日

    Group Relative Policy Optimisation Improves Multilingual Speech Recognition in Low-Resource Languages

    arXiv:2610.30663v1 Announce Type: new Abstract: Autoregressive encoder-decoder speech recognition models trained with teacher forcing can suffer from exposure bias, limiting their ability to recover from their own prediction errors at inference time. Recent work has shown that reinforcement learning with Group Relative Policy Optimisation (GRPO) improves spe

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17. arXiv · 原文发布 2026年10月22日

    Is Anyone Enforcing the Cartel? What Reinforcement Learning Teaches LLM Agents in Markets

    arXiv:2610.28787v1 Announce Type: new Abstract: Evaluations of large language model (LLM) agents report that pricing agents collude at others' expense. Yet collusion is defined by enforcement: a cartel, in which competitors share monopoly profits, holds only if a member who cheats is punished. We introduce an audit that tests enforcement by forcing a member

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18. arXiv · 原文发布 2026年10月22日

    LIFT: Scoped Constraints for Evaluating and Training Instruction Following

    arXiv:2610.28819v1 Announce Type: new Abstract: Common instruction following (IF) benchmarks, such as IFEval, evaluate the ability of language models (LMs) to follow simple constraints that can easily be verified programmatically. These same constraints, in turn, are used as reward signals for reinforcement learning. This design may lead to reward hacking of

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19. arXiv · 原文发布 2026年10月20日

    Training-Free Prompt Selection for Efficient Reinforcement Learning With Partial Rollouts

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20. arXiv · 原文发布 2026年10月20日

    Preserving Reasoning Diversity in Reinforcement Learning with Verifiable Rewards via Adaptive Replay

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21. MIT News · 原文发布 2026年10月2日

    Computational tools for society’s most complex challenges

    Associate Professor Cathy Wu uses reinforcement learning to help map out improvements to transportation and other multifaceted systems.

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22. Hacker News · 原文发布 2026年7月25日

    Show HN: Cubic Doggo 06R: 12-DOF 4-Legged Robot with IMU

    This repo is an upgrade to my previous project, Cubic Doggo: https://github.com/SphericalCowww/CubicDoggo It has since implemented an IMU and several mechanical reinforcements. The next step will be simulation and reinforcement learning.

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23. Hacker News · 原文发布 2026年4月7日

    Elon Musk, Quantum Microtubules, and the Race for the Conscious Machine

    A recent Quanta Magazine piece by John Pavlus examines a burning question: how close are humanoid robots to becoming more sophisticated than humans? While neural networks on fast GPUs have turbocharged computer vision and reinforcement learning, allowing robots to perceive environments better, a massive gap remains between "moving" and "being.

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研究优先级 66 / 100

此分数用于排列研究候选，不是搜索量、预测结果或经过验证的商业需求。

• 证据数量

  25.4 / 30

  去重文章或首次观测；重复抓取不加分。

• 发布主体多样性

  16 / 25

  同主体保守归并，转载不增加独立证据。

• 可比变化

  0 / 20

  仅完整且可比的两周参与；零基线不计算百分比。

• 证据时效

  15 / 15

  依据最新发布或首次观测，不按重复采集时间刷新。

• 名称明确度

  9.7 / 10

  明确实体链接和技术概念优先于含义不明的缩写。

局限与解读

• 首次观测是采集记录的起点，不能证明词语或产品在全球首次出现。
• 计数仅代表已监测的公开来源，不代表全网热度或搜索需求。
• 本站首次发现时间不等于名称或概念的诞生时间。
• 候选来自公开标题、摘要和实体链接，并非已核实的全文分析。
• 来源多样性采用保守归并；未披露的转载或共同所有权仍可能无法识别。
• 重复上榜是观测记录，不是新增文章或独立背书。
• 已有概念或常见产品名，仅保留为历史基线，不视为新创造的词。
• 两周均需足够且可比较的采集覆盖，才判断增长。
• 该词的来源构成发生变化，或缺少可比较的基线。
• 近期证据包含发布三天后才采集的内容，暂不据此判断增长。