Bo Liu (Benjamin Liu)

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@Benjamin_eecs

Incoming PhD @StanfordNLP | BS @PKU1898 | Building continually self-improving AI | Prev @DeepSeek_AI @AIatMeta | DeepSeek-V1/V2/VL/Prover ALE SPIRAL SPICE SPADE

South Korea benjamin-eecs.github.io Joined February 2022

620 Following 1.8K Followers 366 Posts

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Bo Liu (Benjamin Liu) @Benjamin_eecs Aug 20

Continuous self-improvement needs an ever-expanding supply of training environments (goals).

SPADE: one model self-plays the Environment Designer and the Reasoning Agent, writing executable, agentic environments that get harder as it improves. Environment scaling on its own. ♠️

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Bo Liu (Benjamin Liu) @Benjamin_eecs Oct 14

a minimal RSI would be: a coding agent writes the repo that trains its successor, the pretrain, mid-train and post-train recipe plus a new harness.

As a follow-up to SPADE, AutoEnvScaling automates one piece of that repo: post-training environments and curriculum for the terminal domain. The environment designer is now a coding agent: it reads the reasoning agent's rollouts, then builds, runs and fixes each environment in a sandbox. Building a terminal task and solving one are both coding, so one model plays both roles and gets better at both. Each checkpoint builds the tasks that train the next. Qwen3.6-35B-A3B goes from 41.1 to 53.3 on Terminal-Bench 2.1 :))

Simon Yu Verified account @simon_ycl Oct 14

Recursive self-improvement needs agents that can build their own training environments. But researchers still part of the loop, making it hard to scale.

We introduce AutoEnvScaling, which turns Data Flywheel into a terminal task: a proposer agent builds complete Harbor tasks and sandbox. It builds just like human researchers. This enables self-play RL for terminal agents: one model improves as both builder and solver, without human-in-the-loop.

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Bo Liu (Benjamin Liu) @Benjamin_eecs Oct 8

Honored that our SPICE made it into this year's @stateofaireport :)) A self-improving system needs to ground its training signal in interaction with the external world to keep improving. Thanks @nathanbenaich!

Nathan Benaich Verified account @nathanbenaich Oct 8

welcome to the 9th annual @stateofai report!

ai building better ai, world models, physical ai, geopolitics, cyber defense and 12-month predictions.

read it at stateof(dot)ai or watch my editor’s cut here.

a few of this year’s biggest findings follow:

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Bo Liu (Benjamin Liu) @Benjamin_eecs Oct 2

Research taste will matter more and more as AI starts doing research on its own. Part of taste is knowing which old papers to build on. ScholarCatalyst is a first step toward measuring that automatically :))

Yoonho Lee Verified account @yoonholeee Oct 2

Great researchers have an uncanny ability to make connections that seem inevitable in hindsight, in places nobody else would have thought to look. Can we measure this ability?

Introducing ScholarCatalyst: a far-from-saturated benchmark for finding what we call "catalyst papers"📚, labeled by 184 lead authors on 207 of their own recent projects.
Paper: arxiv.org/abs/2610.02202

To make sustained progress on open-ended problems, I think agents need the sort of "research taste" that great researchers have. They need to make deep connections between earlier discoveries and problems those discoveries weren't intended to solve. ScholarCatalyst is a first step towards this goal.

More details in the thread below🧵

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Bo Liu (Benjamin Liu) @Benjamin_eecs Sep 1

Check out Reef: open-source infra for model and harness co-improvement at test time :))
github.com/Human-Agent-So…

github.com GitHub - Human-Agent-Society/reef: Infrastructure for continually self‑improving agents Infrastructure for continually self‑improving agents - Human-Agent-Society/reef

Ao Qu Verified account @ao_qu18465 Sep 1

x.com/i/article/2094…
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