Ching-An Cheng

Senior Research Scientist at @Google Research, working on usable theory and algorithms for Reinforcement Learning, Generative Optimization, and Robotics

[Ching-An Cheng @chinganc_rl]

LLM has been struggling to solve search and optimization at scale when feedback is stochastic. We propose a simple solution, POLCA, using text embedding with “provable” guarantee. Excited to see the first theoretically correct work of LLM optimization. Kudos to @XuanfeiRen

[Xuanfei Ren @XuanfeiRen]

🚀 How can we make LLM-based optimization stable and scalable when the feedback signal is stochastic?

Introducing POLCA: a framework for robust, scalable stochastic generative optimization.

Paper: https://t.co/xgdjISRxtE

Code: https://t.co/9TRuyvxVcf
🧵👇 1/

[Ching-An Cheng @chinganc_rl]

Looking for Google research student researcher (PhD student) to work on LLM and agent related learning.

Preferred background: RL/game theory, agentic system, LLM training.

Candidate will work closely with me and @allenainie

Email me if you are interested. 😀

[Nan Jiang @nanjiang_cs]

I have served as AC for NeurIPS every year since 2020. Just declined (with messages adapted from @xuanalogue). At least the organizers owe the community an explanation why they are the only major ML venue adopting such a policy.

[Dan Roy @roydanroy]

Too close to home?

Junior researcher: I’m publishing papers at NeurIPS, my students are happy, but my chair says I’m “not impactful enough.” I don’t know what that means.

Senior researcher: What did you tell them you accomplished last year?

Junior: 3 top-tier papers, a new theoretical result on regret bounds, and an invited talk.

Senior: And what did they hear?

Junior: That I published 3 papers?

Senior: They heard “I added to the publication count, but didn’t bring in grants or visibility for the department.”

Junior: But regret bounds are impactful!

Senior: To who?

Junior: To… theorists?

Senior: Your chair spends 20 minutes a month justifying your position to the dean. Can they use regret bounds to argue for funding?

Junior: …probably not.

Senior: What external metrics did your work move?

Junior: One collaboration, one best paper award, and some citations. We don’t really track grant impact.

Senior: There’s the problem. Half your contributions are invisible by design.

Junior: But theory is necessary. The field would break without it.

Senior: I believe you. The dean doesn’t care.

Junior: That seems unfair.

Senior: It is unfair. It’s also how academia works. Chairs get grilled on grants, rankings, and prestige, not the long-run stability of ML theory.

Junior: So what should I do?

Senior: Reframe. “Secured $500K in funding to explore foundational algorithms” sounds better than “proved a tighter regret bound.”

Junior: But I don’t have that funding.

Senior: Then you’re fighting academic reality without weapons.

Junior: I don’t have time to write grants and still publish.

Senior: Most junior faculty don’t. That’s the trap — you get judged on impact but don’t get impact resources.

Junior: So what do I do?

Senior: Acknowledge the game is rigged, then play it anyway.

Junior: Meaning?

Senior: Build collaborations that attract funding. Tie your theory to hot applied areas. Translate your results into language deans understand.

Junior: That feels political.

Senior: Everything above a certain level is political. The choice isn’t political vs pure. It’s visible vs irrelevant.

Junior: What if my chair still doesn’t care?

Senior: Then you’ve learned your chair doesn’t know how to evaluate theory. That’s a different problem — one you solve by finding a better environment.

Junior: This is harder than just proving good theorems.

Senior: Proving good theorems is table stakes. Surviving academia while proving good theorems — that’s the actual job.

[Ching-An Cheng @chinganc_rl]

Happening now at #RLC2025. Join us if you’re interested in program, agents and RL.

[Shao-Hua Sun @ ICML 🇰🇷 @shaohua0116]

Kicking off #RLC2025 with our Workshop on Programmatic Reinforcement Learning! This workshop explores how programmatic representations can improve interpretability, generalization, efficiency, and safety in RL.

[Brando Miranda @BrandoHablando]

🔄 We were nominated for Oral+top 1 in the MATH-AI workshp at #ICML!

🚨Why? ≈46 % of GitHub commits are AI-generated—but can we verify them correct?
📢 VeriBench challenges agents; turn Python into Lean code!
🧵1/14
📃 Paper: https://t.co/QPCxg5lKM4

[Ching-An Cheng @chinganc_rl]

We are organizing a workshop tomorrow at #icml25. Come join us and checkout the latest on programmatic representation and agent learning

[Shao-Hua Sun @ ICML 🇰🇷 @shaohua0116]

Our #ICML2025 Programmatic Representations for Agent Learning workshop will take place tomorrow, July 18th, at the West Meeting Room 301-305, exploring how programmatic representations can make agent learning more interpretable, generalizable, efficient, and safe! Come join us!

[Shao-Hua Sun @ ICML 🇰🇷 @shaohua0116]

Our #ICML2025 Programmatic Representations for Agent Learning workshop will take place tomorrow, July 18th, at the West Meeting Room 301-305, exploring how programmatic representations can make agent learning more interpretable, generalizable, efficient, and safe! Come join us!

[Ching-An Cheng @chinganc_rl]

Starting my #ICML2025. Will be here until Saturday. Looking forward to meeting everyone 😀

[Allen Nie ✈️ ICML 2026 🇰🇷 @allenainie]

Provably Learning from Language Feedback

TLDR: RL theory can help us do better inference-time exploration with feedback.

Work done with @wanqiao_xu, @ruijie_zheng12, @chinganc_rl, @adityamodi94, @adith387

📰 https://t.co/Zi3EwmX98R
📍EXAIT Best Paper/Oral Sat 8:45-9:30 am

[Ching-An Cheng @chinganc_rl]

Super excited about this work done by our former intern @wanqiao_xu . We show Learning from Language Feedback (LLF) with LLM can be formally studied with provable no-regret learning algorithms. This result builds a foundation toward new theories for LLM learning and optimization.

[Allen Nie ✈️ ICML 2026 🇰🇷 @allenainie]

Decision-making with LLM can be studied with RL! Can an agent solve a task with text feedback (OS terminal, compiler, a person) efficiently? How can we understand the difficulty? We propose a new notion of learning complexity to study learning with language feedback only. 🧵👇

[Allen Nie ✈️ ICML 2026 🇰🇷 @allenainie]

Decision-making with LLM can be studied with RL! Can an agent solve a task with text feedback (OS terminal, compiler, a person) efficiently? How can we understand the difficulty? We propose a new notion of learning complexity to study learning with language feedback only. 🧵👇

[Ching-An Cheng @chinganc_rl]

Check out this new optimization framework (https://t.co/sN6E1jWl4n) by #DataRobot that can automatically search for "Pareto-optimal" solutions for agentic workflows. It's built on our LLM generative optimization framework #Trace. Excited to see more applications of #Trace! 😎

[Shao-Hua Sun @ ICML 🇰🇷 @shaohua0116]

Our ICML & RLC workshops welcome contributions using programmatic representations as policies, reward functions, skill libraries, task generators, environment models, etc., to improve interpretability, generalization, efficiency, & safety in agent learning & RL! Please retweet 🙏

[Ching-An Cheng @chinganc_rl]

Organizers: Shao-Hua Sun @shaohua0116, Levi Lelis
@levilelis, Xinyun Chen @xinyun_chen_, Shreyas Kapur
@shreyaskapur, Jiayuan Mao @maojiayuan, Ching-An Cheng @chinganc_rl, Anqi Li @AnqiLi24, Kuang-Huei Lee @kuanghueilee, and Leslie Kaelbling

[Ching-An Cheng @chinganc_rl]

We're organizing workshops on Programmatic Representation for Agent Learning at the upcoming #ICML2025 and #RLC2025. We welcome contributions using programs as policies, reward functions, skill libraries, task generators, environment models, etc., and more! See you soon!😀

[Ching-An Cheng @chinganc_rl]

Workshop on Programmatic Reinforcement Learning (RLC 2025)
- Web page: https://t.co/a8h4YObjUE
- Submission Deadline: May 30, 2025, AoE
- Author Notification: June 15, 2025, AoE
- Workshop Date: August 5, 2025 @ Edmonton, Canada

[Ching-An Cheng @chinganc_rl]

Started my new job at #Google Research recently. Super excited about what can be done here. 😎

[RL_Conference @RL_Conference]

The RLC accepted workshops list is out (link in next tweet)!
Programmatic RL
Causal RL
RL and videogames
Inductive biases and RL
and returning from last year: RL beyond rewards, finding the frame, and RL in practice!