目录
- CIC: Contrastive Intrinsic Control for Unsupervised Skill Discovery
- auto-curriculum learning (Jiang et al., 2021b)
- Meta-Motivo（Tirinzoni 等人，2025），zero-shot goal-conditioned RL
- Unsupervised Skill Discovery via Recurrent Skill Training
- Learning to Discover Skills through Guidance
- One After Another: Learning Incremental Skills for a Changing World
- Direct then Diffuse: Incremental Unsupervised Skill Discovery for State Covering and Goal Reaching
- 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
CIC: Contrastive Intrinsic Control for Unsupervised Skill Discovery
- 来源：想起来，想看一下。
- arxiv：https://arxiv.org/abs/2202.00161
auto-curriculum learning (Jiang et al., 2021b)
来源是 RSD。似乎可以做自动 curriculum learning，或许是有启发性的。
Meta-Motivo（Tirinzoni 等人，2025），zero-shot goal-conditioned RL
来源是 RGSD。可能包含一个技能库，也想看。速读一下就行。
Unsupervised Skill Discovery via Recurrent Skill Training
Learning to Discover Skills through Guidance
One After Another: Learning Incremental Skills for a Changing World
Direct then Diffuse: Incremental Unsupervised Skill Discovery for State Covering and Goal Reaching
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。
