Beidi Chen

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@BeidiChen

Asst. Prof @CarnegieMellon, @amazon Scholar, Prev: Visiting Researcher @Meta, Postdoc @Stanford, Ph.D. @RiceUniversity, Large-Scale ML, a fan of Dota2.

infini-ai-lab.cmu.edu Joined November 2011

416 Following 17K Followers 626 Posts

Latest posts 5

Beidi Chen @BeidiChen 6d

Check out the new version of Hermit (v0.2.0)! Now we support 12 harness evolution algorithms, and you can evolve your harness directly with your coding plan.

Infini-AI-Lab Verified account @InfiniAILab Nov 5

Hermit v0.2.0 is out. 🐚🦀

1. Harness evolving with your coding plan: 𝐧𝐨 𝐀𝐏𝐈 𝐤𝐞𝐲 𝐧𝐞𝐞𝐝𝐞𝐝.

2. Now supporting 𝟏𝟐 𝐡𝐚𝐫𝐧𝐞𝐬𝐬 𝐞𝐯𝐨𝐥𝐮𝐭𝐢𝐨𝐧 𝐦𝐞𝐭𝐡𝐨𝐝𝐬: Prompt-only, GEPA, SkillOpt, MWM, Harness², Continual Harness, ReHarness, Meta-Harness, ACE, AHE, RRSI, EvoSafeHarness. (More to come!)

3. A new agent abstraction that makes adding methods much easier.

𝐆𝐢𝐭𝐇𝐮𝐛: github.com/Infini-AI-Lab/…

𝐎𝐫𝐢𝐠𝐢𝐧𝐚𝐥 𝐑𝐞𝐥𝐞𝐚𝐬𝐞 𝐀𝐫𝐭𝐢𝐜𝐥𝐞: x.com/i/article/2114…

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Beidi Chen @BeidiChen Oct 28

“Won’t OAI/Anthropic do that next year?”

I hear this a lot when discussing research. Apparently, the literature review now includes next year’s release notes 😅

That’s why the recent neolab releases have been such a morale boost. It’s exciting to see focused teams and fresh ideas go far with *comparatively* small resources.

Academic labs can be smart about where we contribute, too: challenge an assumption, find an overlooked bottleneck, make an expensive approach practical. Choosing the right question is itself part of the innovation.

Rooting for all my neolab friends. The world would be far too boring if we already knew where every breakthrough would come from. 🚀

Beidi Chen @BeidiChen Oct 28

Tokenization is an architectural decision disguised as preprocessing. @_albertgu, making the units of abstraction learnable is a compelling way to rethink both efficiency and learning beyond text.

Albert Gu Verified account @_albertgu Oct 28

I've always believed that intelligence should come from simple, general building blocks that learn their own abstractions directly from raw data; in other words, the bitter lesson. I think we're finally seeing a real step towards AI that can learn from all data.

Language modeling on raw bytes is a microcosm of this ideal, and to me it's a central problem in AI: if we can't build LLMs that learn end-to-end from bytes, how will we handle the rest of the world's data that we understand even less?

Last year we introduced hierarchical networks (H-Nets) as a first step. Our new version finds up to 3-5x efficiency gains in controlled scaling law experiments, and at larger scale matches an open-source model trained on 11T tokens with ~10x less data and compute. The posts below walk through what we found and why we think it works.

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Beidi Chen @BeidiChen Oct 26

An agent that repeats the same mistake 100 times is just automating déjà vu 😄

We’re excited to open-source Hermit 🐚: a unified training framework for running existing harness evolution methods and building your own.
Same model. Same harness. Better habits.

Infini-AI-Lab Verified account @InfiniAILab Oct 26

We are excited to introduce 𝐇𝐞𝐫𝐦𝐢𝐭 🐚: an open-source framework for training, evaluating, and deploying harness adapters.

Harness evolution is a key building block for RSI and continual learning, enabling agents to continually improve how they work from experience.

Train a reusable external layer. Use existing evolution methods or build your own through one standard framework.

GitHub: github.com/Infini-AI-Lab/…

1/5

#HarnessEvolution #ContinualLearning #RSI #Agent

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Beidi Chen @BeidiChen Oct 12

As data centers hit power limits, AI model needs an energy-efficient scaling axis. ⚡

Embedding scaling grows model capacity with minimal additional serving energy. STAPLE brings this to pretrained LLMs—without restarting pretraining from scratch.

Infini-AI-Lab Verified account @InfiniAILab Oct 12

🧠 How can we keep scaling LLMs with minimal changes in their serving energy?

DeepSeek’s Engram and our concurrent work STEM showed the promise of static sparse memory: large embedding tables with efficient access. But can mature pretrained models learn to use a newly added memory within a limited training budget, avoiding costly pretraining from scratch?

🚀 Introducing STAPLE: a recipe to extend pretrained models' abilities with static sparse memory. Serving energy per token stays close to dense and >50% below MoE upcycling in high-throughput regime.

Scalable capacity, interpretable memory, and compatibility with MoE upcycling - without any meaningful increase in serving cost. 🧵

Paper: arxiv.org/abs/2610.12599
Code: github.com/Infini-AI-Lab/…
Website: infini-ai-lab.github.io/STAPLE/

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