ML Reading Group

Fridays  ·  5-7 PM
Current Topic of the Month Active
Continual Learning
April 2026  ·  Next meeting: April 10th, 5-7 PM

Welcome

We are an informal machine learning reading group here at UC Berkeley. Our goal is to provide a low-stakes space for people to engage with and better understand modern ML research. We are structured around a topic of the month format. Each month, we pick a focused research area and read deeply into it, covering foundational papers, recent advances, and open questions. At every meeting, a subset of people volunteer to present a paper to the group. Those who don't present participate by asking questions and engaging in the group discussion.

We don't have formal prerequisites, but you'll get the most out of discussions if you have exposure to mathematical ML fundamentals, the kind covered in CS 189, CS 182, or similar. If you're newer to reading research papers, don't worry! This is a skill the group helps you build.

Overall, the group is pretty low-stakes and informal, and we are not affiliated with any orgs on campus or the department. If this sounds interesting to you, feel free to reach out using the link below!


This Past Week...

Topic: Reliable AI (March)


Recent Meetings


Paper Selection Criteria

We prioritize papers that are:

You don't need to follow every proof to contribute to the discussion.

Logistics

When: Fridays, 5–7 PM
Where: Contact Vijay for location by clicking the link below


About the Organizer

Photo of Vijay Kethana

Hi everyone, my name's Vijay, and I'm a third-year computer science major. I'm interested in natural language processing (NLP), machine learning systems, and AI in general. Outside of school, I enjoy cooking and hiking.
vkethana@berkeley.edu


All Meetings

Every paper we've covered
Meeting Notes
Graciously compiled by member Lawrence Rudy. While these notes are not comprehensive or perfect, they are very helpful for catching up on past meetings. A formal set of LaTeXed reading group notes is in the works.
View meeting notes archive

Current Apr 2026

Continual Learning

Apr 10, 2026

Topic of the Month: Continual Learning

Meeting notes coming soon.


Past Mar 2026 · Topic of the Month

Reliable AI

Mar 20, 2026

  • Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks
  • CoT May Be Highly Informative Despite "Unfaithfulness" (from METR)

Past Feb 2026 · Topic of the Month

Representation Learning for Reinforcement Learning

Feb 27, 2026

  • Mastering Diverse Domains through World Models
  • Masked Contrastive Representation Learning for RL (M-CURL)

Feb 13, 2026

  • Contrastive Unsupervised Representations for RL (CURL)
  • Bootstrapped Representations in RL
  • Learning Invariant Representations for RL without Reconstruction

Archive Sep 2025 – Jan 2026

Before the topic-of-the-month format, meetings covered a wide range of papers each week.

Jan 26, 2026

  • Native Parallel Reasoner (NPR)

Dec 4, 2025

  • Graph Attention Networks
  • Efficient Streaming Language Models with Attention Sinks
  • ThreadWeaver

Nov 11, 2025

No notes exist for this meeting.

Oct 30, 2025

Oct 23, 2025

  • Multiverse: Your Language Models Secretly Decide How to Parallelize and Merge Generation
  • Intro to TRPO

Oct 16, 2025

  • DDPM (cont.; more on KL Divergence and flow matching)
  • Learning Adaptive Parallel Reasoning with Language Models
  • Intro to Policy Gradients (REINFORCE + Actor-Critic)

Oct 9, 2025

  • DDPM (cont.; flow matching)
  • Intro to GNNs (cont.)
  • Darwin Godel Machine: Open-Ended Evolution of Self-Improving Agents

Oct 2, 2025

Sep 25, 2025

  • DDPM basic problem setup, ELBO, forward and backward processes, motivations
  • Small Language Models are the Future of Agentic AI
  • Intro to Policy Gradients from basics up to policy gradient methods, ending just before REINFORCE

Sep 11, 2025

Topic of the Month

Monthly focus, suggested papers, and formulations

Current Topic Apr 2026

Continual Learning

Continual learning = models that learn from experience over time, as opposed to static models that are trained once and cannot update ''online'' from new user feedback or interactions.


Suggested Papers


Some common formalisms and ideas

Coming soon...