Amir-massoud Farahmand

@sologen.bsky.social

Research Goal: Understanding the computational and statistical principles required to design AI/RL agents. Associate Professor at Polytechnique Montréal and Mila. 🇨🇦 academic.sologen.net

My book, Reinforcement Learning from Human Feedback is done! This is the book I wish I had when learning to fine-tune, align, & now post-train models since ChatGPT. The resource has been built by me finding time to study and document the fundamentals on nights and weekends since 2024.

Bild

This platform will not replace Twitter/X for us (scientists, researchers, profs, etc.). It might be (noticeably) better in several aspects, but (1) it is not a disruptive innovation, and (2) has the third mover disadvantage. P.S: I'll stay active here for now.

It is interesting that we still don't have a completely clear picture of why/when (Optimisitic) Policy Iteration + Monte Carlo estimate works, especially with every-visit update model (which can be biased though consistent, BTW).

Temporal Difference Learning for Diffusion Models (ICML 2026) arxiv.org/abs/2606.15048 By Yangchen Pan (my former PhD student) and co-authors. It reformulates diffusion training as a Markov reward process and introduces a TD objective to encourage temporal consistency across denoising steps.

Temporal Difference Learning for Diffusion Models

Diffusion models are typically trained with objectives that focus on local denoising targets at individual time steps (or adjacent pairs), which do not enforce consistency between predictions along th...

arxiv.org

Sad to hear the passing of Dimitri Bertsekas (1942- 2026). His work has been very influential to me and shaped the way I think about RL. I am sure this is the case for many others in the RL, Control, and Optimization communities.

Do you use often use PPO, but wish you could use something just better? Try REPPO: Relative Entropy Pathwise Policy Optimization! Project Page: cvoelcker.de/projects/rep...

Bild
Marcel Hussing@marcelhussing.bsky.social · 3mo ago

At #ICLR2026 presenting our first poster in the morning on Relative Entropy Pathwise Policy Optimization. Stop by at #4613. 🧑‍🎓 @cvoelcker.bsky.social, @axelbrunnbauer.bsky.social, Michal Naumann, Pieter Abbeel, @ericeaton.bsky.social, Radu Grosu, @sologen.bsky.social @igilitschenski.bsky.social

Hypothesis: People have been gradually shifting to write more like ChatGPT and alike. They use structures such as "This is not only X; but it is also Y". These struct. are natural part of lang, but either 1. they're becoming more prevalent, 2. I've become more sensitive to them.

I am rerunning my class on robot learning this year, and I plan to push many code examples to help others get to the ugly details fast. One of these details is how BC gets off track as network sizes change. Blog and notebook below.

Bild

A significant hurdle of the empirical RL and the broader AI research is caused by the limitations of the environments in which our agents learn and build their "artificial minds". This should be compared with the richness of the real-world in which a human child flourishes.