rs intern @ meta, phd @Mila_Quebec, prev. @UWaterloo i like generative models and toronto sports teams (not necessarily in that order)
Montréal, Canada
Joined November 2016
- Tweets217
- Following774
- Followers280
- Likes772
Avery Ryoo retweeted
1/4) We're hiring in my lab, **please share**! 🚀
There are two postdoc opportunities in cog neuro and AI.
This is for a new project supported by a Schmidt Polymath Award. This project aims to build an engaging video game to collect data for neurofoundation models. 🧠🤖
Avery Ryoo retweeted
Most papers treat the privileged information in OPSD as a fixed parameter and ablate other things around it. When OPSD fails they call the algorithm doomed or a waste of time, without actually touching the privileged information once.
It was pretty clear form our initial work on this that the quality of privileged information is the most important factor to make OPSD work (arxiv.org/pdf/2602.04942).
@zkjzou did the most comprehensive analysis on this figuring out what acc makes OPSD tick and why it may not be doomed.
This is the first work that convinced me OPSD when done right can actually consistently outperform GRPO.
Not All Privileged Information Is Equal: What Makes OPSD Actually Work?
We find that information-rich PI, such as environment observations and mistake analysis, outperforms PI that only provides ground-truth solutions.
Better PI leads to better post-training performance. But can we predict which PI will work best before spending compute on post-training?
We investigated three offline metrics:
🔹 Utility: A model's performance with PI can be misleading due to information leakage.
🔹 Reverse KL: Larger PI-induced distribution shifts often correlate with better post-training performance, but incorrect PI can cause equally large shifts.
🔹 Error-step KL contrast: Measuring whether PI changes the model's behavior specifically at error steps better captures its effectiveness and helps select stronger PI.
What types of privileged information have you found most effective in your OPSD experiments?
📄 Paper: arxiv.org/abs/2610.22512
Avery Ryoo retweeted
Your neo-lab wants to pre-train a frontier model, but no provider will sell you enough co-located chips?
We got you.
Our new work shows that SOAP & SPlus achieve strong worker scaling to 96+ DiLoCo workers (e.g., 96 providers) by whitening the update’s covariance. 🧵1/9
Avery Ryoo retweeted
Mila's annual supervision request process is now open to receive MSc and PhD applications for Fall 2027 admission! For more information, visit mila.quebec/en/prospective-s…
Avery Ryoo retweeted
Today, we introduce Priming Models, a new paradigm for training one-step generative models from scratch.
No diffusion teacher. No flow map. No CFG. No JVP.
We believe this work will challenge how we think about building one-step models.
Joint work with @sitanch, @du_yilun, @parkwbin03, Bohyung Han.
🔥 1.19 FID one-step on ImageNet 256×256 while using substantially less compute, e.g., 12X less than Drifting-B, 6X less than Drifting-L
🎨 Scales to text-to-image generation (GenEval 0.82)
🤖 World modeling on RT-1 / Bridge-V2
Priming Model is built on one core message: [Reconstruction pretraining --> Distributional post-training].
Avery Ryoo retweeted
Really excited to see this progress! We’ve been exploring some of these ideas in our work on neuro-foundation models. Glad to see these models enabling live BCI use.
This new post from @neuralink combines POYO-style spike tokenisation with a Mamba backbone, akin to our POSSM architecture, and explores self-supervised learning for these models, as we did recently with MOJO. Exciting to see these ideas supporting real-time BCI use! 🧵
Avery Ryoo retweeted
This new post from @neuralink combines POYO-style spike tokenisation with a Mamba backbone, akin to our POSSM architecture, and explores self-supervised learning for these models, as we did recently with MOJO. Exciting to see these ideas supporting real-time BCI use! 🧵
Our clinical trial participants have now used their Neuralink implants for over 50,000 hours, building one of the largest datasets of intracortical neural activity ever.
We used it to pretrain a foundation model for BCI: self-supervised neural encoders that turn noisy, drifting spikes into stable representations.
Some initial results:
• Decoders that keep working for weeks instead of days. For some users, calibration dropped from 10 min/day to 10 min/week
• Teleporting cursors that jump straight to the intended target
• A new cursor-control record of 11.32 bits per second (exceeding the performance of most able-bodied people)
We’re just getting started. Future directions include pooling data across participants, one-shot (or zero-shot) calibration, and an API for the motor cortex.
Read more:
neuralink.com/updates/pretra…
Avery Ryoo retweeted
i hate to give the NBA grifters the attention they’re so obviously craving, but the fact that there are currently zero trans women in the WNBA makes it clear that this has never been about “protecting” anyone. it’s about exploiting the league’s growth to advance their anti-trans agenda. and it’s pathetic
Avery Ryoo retweeted
Does a voice agent's quality come from the orchestration framework or the models underneath?
We built 𝐕𝐀𝐦𝐨𝐒 𝐁𝐞𝐧𝐜𝐡 to measure it.📏
Holding the models fixed and swapping the harness moved completion 1.7 pts. Swapping the model stack moved it up to 28. 🧵🧵
Avery Ryoo retweeted
When talking to LLMs, It’s not What You Say, but How You Say It!
Check out our new ACL paper! 🌀
Does the *way* we say something to an LLM affect whether the LLM agrees with it?
Our ACL 2026 paper - “It’s Not What You Say, It’s How You Say It”, answers with a resounding yes! ✅
aclanthology.org/2026.acl-lo…
Co-led with @ClaraKuempel, as well as @MichelleWastl and @a_stadt 🧵
Avery Ryoo retweeted
New paper!
Self-Distillation with sampled demonstrations leads to reduced output diversity.
All because the teacher can more easily align some solution–demonstration pairs than others.
with @mpezeshki91* @AaronCourville* at @AIatMeta @Mila_Quebec
Avery Ryoo retweeted
Excited to introduce our new paper exploring the use of in-context learning to support intrinsic curiosity! This was with an amazing team at Google including @sangnie, @g_lajoie_, @derifatives, @neurosacramento, Rajaj Nasser, @oswaldjoh, and @blaiseaguera. arxiv.org/abs/2606.19476
Avery Ryoo retweeted
🚨Excited to announce our workshop Context Beyond the Window hosted at COLM in SF! 🚨
LLMs have finite context windows, yet real-world tasks demand absorbing, retaining, and acting on information that far exceeds any single prompt.
1/3
We're looking for submissions across:
context-beyond-window.github…
• Context compression 🧃 — token compaction, recursive subagent calls, and external memory for storing and retrieving information
• Efficient architectures 🚀 — sub-quadratic attention variants that make extremely long context computationally feasible
• Continual training 🌱 — test-time training on streaming data, context distillation, and knowledge accumulation through continued pre-training
• Agentic memory systems 🐘 — scaffolds and test-time scaling techniques that improve knowledge retention and acquisition in LLMs
• Evaluation 🎯 — benchmarking models on increasingly long-horizon tasks
ALT Modern language models operate within finite context windows, yet many real-world tasks require models to absorb, retain, and act on information that far exceeds any single prompt. This workshop addresses the full spectrum of context management: fitting more into the window, maintaining state across interactions, and transferring knowledge into parameters. We frame this around the trade-off between context-time memory (information supplied at inference) and weight-time memory (information absorbed into parameters). Our goal is to build a shared vocabulary across subcommunities that rarely meet in one venue: long-context modeling, retrieval-augmented systems, continual learning, knowledge distillation, and LLM agents.
Avery Ryoo retweeted
The Montreal Expos are exiting the baseball space. During Q2 and Q3 2026, we will transition to acquiring high-performance GPU assets. This is all part of our long-term vision to become a fully integrated GPU-as-a-Service (GPUaaS) and AI-native cloud solutions provider.
Avery Ryoo retweeted
How do neural circuits in the brain implement normalization? 🧠
In our new paper, we show that just normalizing sensory input isn't enough. Crucially, we must also normalize the error signals! 🧵👇
Paper: arxiv.org/abs/2603.17676
Avery Ryoo retweeted
very important to fly out of Toronto’s Pearson airport at least once in your life so you can experience what it’s like to be inside an airport that personally and specifically hates you
Avery Ryoo retweeted
Montreal deep tech scene is getting hot!! Many recent hires of Cohere, Mistral, Periodic Labs, Poolside are all based in Montreal. And now, AMI will have an office here 🔥
It's a no-brainer, though. @Mila_Quebec has the highest concentration of deep learning expertise with interdisciplinary connections.
Thanks to recent US regulation changes on immigration, no more brain drain! Let's build more in Canada!
Unveiling our new startup Advanced Machine Intelligence (AMI Labs).
We just completed our seed round: $1.03B / 890M€, one the largest seeds ever, probably the largest for a European company.
We're hiring!
[the background image is the Veil Nebula - a picture I took from my backyard, most appropriate for an unveiling]
More details here:
techcrunch.com/2026/03/09/ya…
