当前位置: 首页 > news >正文

论文速读记录 | 2025.10



目录
  • Horizon Generalization in Reinforcement Learning
  • HIQL: Offline Goal-Conditioned RL with Latent States as Actions
  • Contrastive Preference Learning: Learning from Human Feedback without RL
  • Controlled Diversity with Preference: Towards Learning a Diverse Set of Desired Skills
  • Human-Aligned Skill Discovery Balancing Behaviour Exploration and Alignment
  • Few is More: Task-Efficient Skill-Discovery for Multi-Task Offline Multi-Agent Reinforcement Learning
  • SMAC-R1: The Emergence of Intelligence in Decision-Making Tasks
  • Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables
  • VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning
  • Rethinking Reward Modeling in Preference-based Large Language Model Alignment
  • DOPL: Direct Online Preference Learning for Restless Bandits with Preference Feedback
  • Fewer May Be Better: Enhancing Offline Reinforcement Learning with Reduced Dataset
  • Data Center Cooling System Optimization Using Offline Reinforcement Learning
  • SpikeLLM: Scaling up Spiking Neural Network to Large Language Models via Saliency-based Spiking
  • Rethinking Inverse Reinforcement Learning: from Data Alignment to Task Alignment
  • Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?
  • Why Distillation can Outperform Zero-RL: The Role of Flexible Reasoning
  • Thinkless: LLM Learns When to Think
  • Learning to Reason without External Rewards


Horizon Generalization in Reinforcement Learning

  • arxiv:https://arxiv.org/abs/2501.02709
  • website:https://horizon-generalization.github.io/
  • 来源:Benjamin Eysenbach 的新作,是一篇 arxiv paper,同学说有趣。
  • 主要内容:

HIQL: Offline Goal-Conditioned RL with Latent States as Actions

  • arxiv:https://arxiv.org/abs/2307.11949
  • website:https://seohong.me/projects/hiql/
  • 来源:合作者推荐的文章,好像也是 Benjamin Eysenbach 发表的。

Contrastive Preference Learning: Learning from Human Feedback without RL

  • arxiv:https://arxiv.org/abs/2310.13639
  • GitHub:https://github.com/jhejna/cpl
  • 来源:无意中搜到的文章,ICLR 2024,好像之前读过。
  • 主要内容:

Controlled Diversity with Preference: Towards Learning a Diverse Set of Desired Skills

  • arxiv:https://arxiv.org/abs/2303.04592
  • 来源:[mask]

Human-Aligned Skill Discovery Balancing Behaviour Exploration and Alignment

  • arxiv:https://arxiv.org/abs/2501.17431
  • 来源:[mask]

Few is More: Task-Efficient Skill-Discovery for Multi-Task Offline Multi-Agent Reinforcement Learning

  • arxiv:https://arxiv.org/abs/2502.08985
  • 来源:同学的最新工作。
  • 主要内容:
    • 这篇文章关注的 setting 是 offline multi-task MARL;特别的,agent 只在(比如说)三个人合作的场景上训练,然后就可以泛化到任意多个人合作的场景。同学讲的故事是,用 transformer 作为一个翻译器,把三个人的合作动作翻译为多个人的,感觉这个故事听起来非常好。

SMAC-R1: The Emergence of Intelligence in Decision-Making Tasks

  • arxiv:https://arxiv.org/abs/2410.16024
  • 来源:在知乎看到的,但现在知乎帖子好像找不到了)
  • 主要内容:
    • 用 LLM 生成打 smac 的 python 决策树代码。
    • 具体 method:

Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables

  • arxiv:https://arxiv.org/abs/1903.08254
  • 来源:[mask]
  • 主要内容:
    • 这篇文章提出了 PERAL 方法。

VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning

  • arxiv:https://arxiv.org/abs/1910.08348
  • 来源:[mask]
  • 主要内容:
    • 这篇文章提出了 VariBAD 方法。

Rethinking Reward Modeling in Preference-based Large Language Model Alignment

  • arxiv:https://arxiv.org/abs/2411.04991
  • OpenReview:https://openreview.net/forum?id=rfdblE10qm
  • 来源:ICLR 2025 oral。
  • 主要内容:
    • 这篇文章关注 LLM 的 RLHF。据说不采用 bradley-terry model 来建模 reward model,而是直接训一个分类器,学习一个 (x,y) 是好的还剩坏的,然后使用分类器的概率 logit 作为 RLHF 的 reward。
    • 是否使用了非成对的比较 \((x_1, y_1^+, x_2, y_2^-)\),而非把成对比较 \((x, y^+, y^-)\) 打乱(?)
    • 实验是否过于 toy(?)理论大概说了什么(?)

DOPL: Direct Online Preference Learning for Restless Bandits with Preference Feedback

  • arxiv:https://arxiv.org/abs/2410.05527
  • open review:https://openreview.net/forum?id=2iYVBqRHK4
  • 来源:合作者推荐的文章。
  • 主要内容:
    • preference-based index policy(?)

Fewer May Be Better: Enhancing Offline Reinforcement Learning with Reduced Dataset

  • 来源:师兄的文章。

Data Center Cooling System Optimization Using Offline Reinforcement Learning

  • arxiv:https://arxiv.org/pdf/2501.15085
  • 来源:xianyuan zhan 组的新文章。
  • 主要内容:
    • T-symmetry。

SpikeLLM: Scaling up Spiking Neural Network to Large Language Models via Saliency-based Spiking

  • arxiv:https://arxiv.org/abs/2407.04752
  • 来源:师兄推荐的神秘文章,ICLR 2025 poster。

Rethinking Inverse Reinforcement Learning: from Data Alignment to Task Alignment

  • arxiv:https://arxiv.org/abs/2410.23680
  • 来源:偶然看到的文章。

Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?

  • 来源:师兄偶然提到,系里其他人的文章。

Why Distillation can Outperform Zero-RL: The Role of Flexible Reasoning

  • arxiv:https://arxiv.org/abs/2505.21067
  • 来源:偶然看到的文章。

Thinkless: LLM Learns When to Think

  • arxiv:https://arxiv.org/abs/2505.13379
  • 来源:偶然看到的文章。

Learning to Reason without External Rewards

  • arxiv:https://arxiv.org/abs/2505.19590
  • 来源:偶然看到的文章。


http://www.hskmm.com/?act=detail&tid=23202

相关文章:

  • 【Rust GUI开发入门】编写一个本地音乐播放器(15. 记录运行日志) - Jordan
  • 6 种常见 AI 编程协作便捷的方法总结
  • DeploySharp开源发布:让C#部署深度学习模型更加简单
  • 别样的国庆作业大战
  • ROS2之服务
  • macOS上优雅运行Docker容器
  • 题解:CF1770H Koxia, Mahiru and Winter Festival
  • HarmonyOS之LocalStorage - 详解
  • Spring Boot Logback:实现定时任务日志与业务日志隔离 - Higurashi
  • 网络流 最小割 Dinic算法
  • 15.VLANIF(2025年9月30日) - 教程
  • 树莓派搭建NAS之一:安装系统
  • 新手Markdown学习
  • 马云归来,“新零售”不死 - 指南
  • RNN
  • 10.2笔记
  • Shell / Bash 学习
  • 【Linux 架构探幽:从入门到内核・系统编程开篇】基础指令与权限精讲,筑牢框架制作根基
  • 使用 Dart 进行验证码识别
  • 用 Rust 进行验证码识别
  • teset3
  • Java并发编程(5)
  • 定时任务详解
  • 华为wlan无线配置 - 教程
  • PINN训练新思路:把初始条件和边界约束嵌入网络架构,解决多目标优化难题
  • 可持久化数据结构
  • 2025.10.2——1黄
  • 图的匹配
  • Tarjan 算法
  • Mondriaans Dream题解