@yale | @modal, residency @primeintellect
Joined November 2022
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new blog! What methodologies do labs use to train frontier models?
The blog distills 7 open-weight model reports from frontier labs, covering architecture, stability, optimizers, data curation, pre/mid/post-training + RL, and behaviors/safety
djdumpling.github.io/2026/01…
Alex Wa retweeted
Today we're announcing that Modal has crossed $1B in annualized run rate, as well as raised a $750M Series D at a $15.75B valuation, led by @Accel.
Since our Series C, usage has tripled and we’ve grown more than 15x year-over-year.
We are deeply grateful to the developers and companies building on Modal. There's still so much more to build, and we can't think of a better time to be building software.
Alex Wa retweeted
New research 🚀 from the ACE (Agentic Context Engineering) team at Stanford
We took the failure lessons out of an LLM agent's memory, and it got better! 📈
Why? We find that failure recovery tips could hurt LLM agent performance when they sit in context all the time, even when nothing is going wrong.
So we build Sentry 🛡️: a test-time framework that recovers LLM agents from failures. It keeps failure knowledge outside the agent and only steps in when a failure actually happens.
On average: +39% over our own ACE, +37% over the best runtime-intervention baseline.
📄 Paper: arxiv.org/pdf/2610.02994
💻 Code: github.com/nuglifeleoji/Sent…
Demo Here!!!!!!!
More details below 🧵
(1/N)
Alex Wa retweeted
new blog post! I'm convinced text diffusion is the future, and we'll start to see it everywhere very soon.
varunneal.github.io/essays/d…
Alex Wa retweeted
I just wrapped up my internship @modal.
This summer I worked on container filesystems, speeding up agentic workloads by up to 4x and directory traversals by 12x. I wrote about this systems work behind it here: amitsai.com/writing/erofs/?v…
Huge thanks to @amitpr, @jonobelotti_IO, the runtime team, and everyone at Modal for an amazing summer!
Alex Wa retweeted
Now open source! Have codex/cc set it up for you and read the code behind the optimizations and results.
link below to repo and docs
Alex Wa retweeted
It's my last day as an intern on @modal's training team.
With the rise of RL, it's becoming increasingly critical to have robust, reliable observability. I wanted to share some of my work building an observability system for the Modal Training Gym (gym.modal.dev), built on top of @slime_framework & @radixark miles.
Thank you @qjoyliu, @peywalt, training team, and Modal for an awesome summer!
Alex Wa retweeted
It's my last week @modal.
This summer, I had the chance to work alongside @waltergoat, @colin_weld, @paulgb on our Sandbox product. It was my first time working on systems at this scale, and I couldn't be more grateful for their mentorship.
Sandboxes are full of gnarly distributed systems problems. I wrote about one of them - distributed locking - on my blog: scotthao.com/writing/distrib…
To ship it, I needed to break rules for practicality and performance. This led me to formally verify the design with TLA+ to catch the many race conditions.
This is also my first blog post! I don't share my writing often, but I'd like to do it more. Hope you enjoy.
Main takeaway is that when long-horizon self-play rewards didn’t transfer, the rollouts were still useful, as market data. Modeling prices, valuing liquidity, and distilling the resulting decisions worked.
More technical details about the implementation here:
djdumpling.github.io/2026/08…
Huge thanks to @primeintellect and @modal for their gracious compute/API support, particularly @johannes_hage @omouamoua @creet_z @peywalt, and early readers including @omouamoua @varunneal @nanjiangwill @ayfeng23 @laurenjshen for feedback!
This project wouldn’t exist without you all🙏
🧵I post-trained Qwen3-4B-Instruct to play MegaGem, @JaneStreetGroup's new auction game.
It ranks #1 on 3-player MegaGem, above models like GPT-5.5, Claude Opus 4.8, and Gemini 3 Flash, but statistically unresolved with Gemini 3.1 Pro.
However, self-play RL wasn’t what got it there.
Main takeaway is that when long-horizon self-play rewards didn’t transfer, the rollouts were still useful, as market data. Modeling prices, valuing liquidity, and distilling the resulting decisions worked.
More technical details about the implementation here:
djdumpling.github.io/2026/08…
Huge thanks to @primeintellect and @modal for their gracious compute/API support, particularly @johannes_hage @omouamoua @creet_z @peywalt, and early readers including @omouamoua @varunneal @nanjiangwill @ayfeng23 @laurenjshen for feedback!
This project wouldn’t exist without you all🙏