- DiscoRL: Discovering state-of-the-art reinforcement learning algorithms | Nature: David silver last work at deepmind
- MuZero: Schrittwieser, Julian, et al. “Mastering atari, go, chess and shogi by planning with a learned model.” Nature 588.7839 (2020): 604-609.
- DQN: Human-level control through deep reinforcement learning | Nature
- SAC: [1812.05905] Soft Actor-Critic Algorithms and Applications
- A3C: [1602.01783] Asynchronous Methods for Deep Reinforcement Learning
- DDPG: Deterministic Policy Gradient Algorithms
World Models - https://worldmodels.github.io/#:~:text=We%20explore%20building%20generative%20neural,back%20into%20the%20actual%20environment
- Training Agents Inside of Scalable World Models
- Mastering Diverse Control Tasks through World Models
- LeWorldModel: Stable End-to-End Joint-Embedding Predictive Architecture from Pixels | alphaXiv
- MIRA
- DIAMOND: Diffusion for World Modeling: Visual Details Matter in Atari
- [2605.26379] When Does LeJEPA Learn a World Model?
- Fei-Fei Li on X: “https://t.co/Kt50ttQRMJ” / X
JEPA
- The Annotated JEPA | Elements of a Vector Space
- Deep Dive into Yann LeCun’s JEPA | Rohit Bandaru
- A Guided Tour of the Joint-Embedding Predictive Architecture
- GitHub - lucidrains/x-jepa: Explorations into some of the approaches advocated by Yann LeCun, and just a more wholistic architecture (JEPA) in general · GitHub
- VL-JEPA: Joint Embedding Predictive Architecture for Vision-language | alphaXiv
- Agentic World Modeling: Foundations, Capabilities, Laws, and Beyond | alphaXiv
Distillation
- [2306.13649] On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes
- On-Policy Distillation - Thinking Machines Lab
- Reinforcement Learning via Self-Distillation | alphaXiv
- [1910.01108] DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
- [2303.17651] Self-Refine: Iterative Refinement with Self-Feedback
- [2106.01345] Decision Transformer: Reinforcement Learning via Sequence Modeling
- [2305.20050] Let’s Verify Step by Step
Inverse RL:
RL x Diffusion
- Diffusion Models for Reinforcement Learning: Foundations, Taxonomy, and Development | alphaXiv
- Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimization | alphaXiv
- [2603.27450] FlowRL: A Taxonomy and Modular Framework for Reinforcement Learning with Diffusion Policies
Tutorials