Resources
Papers, courses, textbooks, and other resources for the ML/RL Reading Group.
Machine Learning (ML)
- Recursive Language Models
- Transformers without Normalization
- Hardware Scaling Trends and Diminishing Returns in Large-Scale Distributed Training
- ShinkaEvolve: Towards Open-Ended And Sample-Efficient Program Evolution
- Flow Matching for Generative Modeling
Reinforcement Learning (RL)
- Partially Observable Reinforcement Learning with Memory Traces
- CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning
- Revisiting the Minimalist Approach to Offline Reinforcement Learning
- Flow Matching Policy Gradients
- Normalizing Flows are Capable Models for RL
- Behavior Regularized Offline Reinforcement Learning
- Training Diffusion Models with Reinforcement Learning
Reinforcement Learning
- Barto & Sutton Textbook (PDF) Textbook
- David Silver's RL Course Course
- DeepRL Bootcamp Course
- Pieter Abbeel's DeepRL Series Video
- RL Tutorials — Tim Miller Tutorial
- Dr. Pingali's BOOST'24 Lectures Lectures
Neural Networks
- Haykin — Neural Networks, 3rd ed. (PDF) Textbook
- Dr. Pingali's Bertinoro Talk (PDF) Slides
- Dr. Pingali's BOOST'24 Lectures Lectures