Senior Research Scientist at @Google Research, working on usable theory and algorithms for Reinforcement Learning, Generative Optimization, and Robotics
Redmond, WA
Joined March 2020
- Tweets128
- Following103
- Followers2.3K
- Likes294
Pinned Tweet
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
🚀 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: arxiv.org/abs/2603.14769
Code: github.com/rlx-lab/POLCA
🧵👇 1/
Presenting two posters with @allenainie @XuanfeiRen today at #ICML2026
1. “Formalizing Learning from Language Feedback with Provable Guarantees” 10:30am, Hall A 4500
2. “POLCA: Stochastic Generative Optimization with LLM”, 5pm, Hall A 1810
Come check them out!
Just arrived at #ICML2026 Looking forward to meeting everyone once I get passed this long long line of registration …
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. 😀
Ching-An Cheng retweeted
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.
I have declined volunteer review for @NeurIPSConf in light of their policy on sanctioned institutions. If you feel similarly opposed, feel free to adapt my message.
ALT I am declining to review for NeurIPS this year in light of its newly adopted policy to refuse submissions from individuals affiliated at institutions subject to US sanctions. While I am not affiliated with any institution on the list, or a citizen of any sanctioned country, I believe this is an absurd policy that unfairly harms researchers based on arbitrary determinations by the US government, and will also lead to further fragmentation and decoupling of the AI research ecosystem. Indeed, the new policy has already resulted in the China Computer Federation advising all computer scientists who are Chinese nationals to avoid submitting any papers or provide review services for NeurIPS (see https://www.ccf.org.cn/Focus/2026-03-25/865918.shtml) I do not believe this was the intended outcome of the policy. Full text here: https://pastebin.com/LEmwfMZT
Ching-An Cheng retweeted
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.
Junior PM: I'm shipping everything on time, team loves me, but my manager says I'm "not strategic enough." I'm exhausted trying to figure out what that means.
Senior PM: What did you tell him you accomplished last quarter?
Junior PM: Delivered 5 features, reduced tech debt, improved team velocity by 15%.
Senior PM: And what did he hear?
Junior PM: That I delivered 5 features?
Senior PM: He heard "I kept the team busy with stuff that doesn't move numbers I get asked about."
Junior PM: But velocity improvement is strategic.
Senior PM: To who?
Junior PM: To... the team?
Senior PM: Your manager spends 20 minutes a week with his director explaining why you exist. Can he use velocity to justify your headcount?
Junior PM: I... probably not.
Senior PM: What business metrics did those 5 features move?
Junior PM: Three were tech debt, one was a sales request, one was compliance. We don't really measure impact on that stuff.
Senior PM: There's your problem. Half your work is invisible by design.
Junior PM: But that work was necessary. The platform would break without it.
Senior PM: I believe you. Your manager's director doesn't care.
Junior PM: That seems unfair.
Senior PM: It is unfair. It's also how companies work. Your manager gets grilled about revenue and retention, not platform stability.
Junior PM: So I should have said no to the tech debt?
Senior PM: You probably couldn't. But you should have framed it differently.
Junior PM: How?
Senior PM: "Prevented $200K in potential downtime costs" sounds better than "reduced tech debt."
Junior PM: But I don't have that number.
Senior PM: Then you're fighting organizational reality without weapons.
Junior PM: I don't have analytics support or time to instrument everything.
Senior PM: Most junior PMs don't. That's the trap - you get judged on business impact but don't get business resources.
Junior PM: So what do I do?
Senior PM: Acknowledge the game is rigged, then play it anyway.
Junior PM: Meaning?
Senior PM: Make allies in sales and marketing. They have the numbers you need. Shadow customer calls. Connect your work to their goals.
Junior PM: That feels political.
Senior PM: Everything above a certain level is political. The choice isn't political vs pure. It's visible vs irrelevant.
Junior PM: What if I try this and my manager still doesn't care?
Senior PM: Then you learn your manager doesn't know how to evaluate PM work. That's a different problem - one you solve by finding a better manager.
Junior PM: This is harder than just building good products.
Senior PM: Building good products is table stakes. Surviving organizational dysfunction while building good products - that's the actual job.
Ching-An Cheng retweeted
🔄 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: openreview.net/forum?id=rWkG…
We are organizing a workshop tomorrow at #icml25. Come join us and checkout the latest on programmatic representation and agent learning
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 retweeted
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!
Starting my #ICML2025. Will be here until Saturday. Looking forward to meeting everyone 😀
Ching-An Cheng retweeted
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
📰 arxiv.org/pdf/2506.10341
📍EXAIT Best Paper/Oral Sat 8:45-9:30 am
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.
Ching-An Cheng retweeted
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. 🧵👇
Check out this new optimization framework (github.com/datarobot/syftr) 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! 😎
Ching-An Cheng retweeted
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 🙏
Workshop on Programmatic Reinforcement Learning (RLC 2025)
- Web page: prl-workshop.github.io
- Submission Deadline: May 30, 2025, AoE
- Author Notification: June 15, 2025, AoE
- Workshop Date: August 5, 2025 @ Edmonton, Canada
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