Simon Yu

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@simon_ycl

Building Self-Improvement System (AutoEnvScaling, Shepherd, SPADE) | 3rd Year PhD advised by @shi_weiyan | intern in @MSFTResearch | Prev Student @EdinburghNLP

Boston, Massachusetts simonucl.github.io Joined September 2015

984 Following 932 Followers 602 Posts

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Simon Yu @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.

Simon Yu @simon_ycl Oct 20

Synthetic RL environments are only useful if they’re solvable, hard, and verifiable. WildAWM gets all three.

Have always been a fan of Agent World Model, and WildAWM takes it to computer use: fully open-source synthesis of complex environments. Training in them gives +10.58 across 8 benchmarks. Great work by @zhaoywang_CS and team!

Zhaoyang Wang Verified account @zhaoywang_CS Oct 20

🔥 Excited to share Agent World Model in the Wild (WildAWM)! 🌍

Fully open-source synthesis 🔓: No proprietary models needed! We synthesized 341 high-quality environment–task pairs, addressing 3 challenges in task synthesis:

✅ Solvability: At least one agent can successfully complete each task.

🔥 Difficulty: Challenging even for frontier models, with GPT-6-Astra achieving only ~72.7 pass@8.

🎯 Verifier reliability: 95% agreement between task verifiers and agent-as-a-judge ensemble.

📈 Effective RL training: Trained exclusively in these synthetic worlds, Qwen3.6-35B-A3B achieves an average +10.58-point improvement across 8 OOD benchmarks!

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Simon Yu @simon_ycl Oct 19

Thanks for selecting AutoEnvScaling among the papers of of the week!

DAIR.AI Verified account @dair_ai Oct 18

x.com/i/article/2111…
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Simon Yu @simon_ycl Oct 14

You can't scale test-time compute if a single eval takes 28 hours. ⏳

DuoDiscover is a masterclass in long-horizon agent optimization: multi-agents build their own fast local evaluators, and coordinate these agents bottom-up. Amazing work by @dilarafsoylu and team!

Dilara Soylu Verified account @dilarafsoylu Oct 14

AI is cracking math problems. What's holding it back in engineering?

The cost of evaluation. Agents can generate ideas in seconds, but testing them can take hours or days.

💡 Meet DuoDiscover: where evaluation is part of what agents discover 👇🧵

Some highlights:
🏆 #1 on NVIDIA's SOL-ExecBench
🐍 2× the experts on SWE-fficiency
🚀 1.92× on vLLM; 1.44× on FreeToken
🔥 2.68× over production TPU kernels
🖥️ Beats the best human and AI-designed CPU prefetchers

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Simon Yu @simon_ycl Oct 14

🔥🔥

Dongxi 东锡 NLP Verified account @dongxi_nlp Oct 14

推荐阅读！

AutoEnvScaling: Automating the Data Flywheel with Terminal Agents
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