Alan Ritter @alan_ritter
Computing professor at Georgia Tech - NLP, ML, AI aritter.github.io Atlanta, GA Joined March 2009-
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@sos_266 @EthanMendes3 @hyungjoochae @CherylolGuo @icmlconf @gtcomputing Thanks - I just filled out the form!
Excited to attend #ICML2026 in Seoul 🇰🇷 together with my Ph.D. students: @EthanMendes3, @hyungjoochae, @CherylolGuo! We’ll be presenting work on: - Multilingual reasoning - Self-distillation from expert reasoning - Cloning synthetic web environments Poster info + paper links below. Looking forward to catching up!
Here are the papers we'll be presenting: Didactic to Constructive: Turning Expert Solutions into Learnable Reasoning @EthanMendes3 , @jungsoo___park, @alan_ritter arXiv: arxiv.org/abs/2602.02405 Poster: Wed, Jul 8 • 5:00–6:45 PM KST, Hall A (#2502) Safe and Scalable Web Agent Learning via Recreated Websites @hyungjoochae , @jungsoo___park, @alan_ritter arXiv: arxiv.org/abs/2603.10505 Poster: Tue, Jul 7 • 10:30 AM–12:15 PM KST, Hall A (#1714) Learning to Route Languages for Multilingual Preference Optimization @CherylolGuo, Hiromi Wakaki, @mittu1204, @alan_ritter, @cocoweixu arXiv: arxiv.org/abs/2605.25360 Poster: Tue, Jul 7 • 10:30 AM–12:15 PM KST, Hall A (#402)
Knowledge is not evenly distributed across languages. For a given question, what if better information comes from another language? 🔥 We propose Language-Routed Policy Optimization (LRPO), an online reinforcement learning framework that lets LLMs explore and learn from cross-lingual generations during training. #ICML2026
.@gtcomputing's @alan_ritter and his Ph.D. student Ethan Mendes have been named to the 2026 list of @MSFTResearch Fellows and Fellowship Advisors. Their research develops artificial intelligence agents that function as teammates. b.gatech.edu/4uILpL3
Real-world LLM interactions are conversational, yet LLMs are far more vulnerable to multi-turn than single-turn attacks. Introducing DialTree: RL + tree search for automated multi-turn attack discovery. 📊 81.5% avg ASR@1 across 12 LLMs 📈 +44.2% over prior SOTA #ICLR2026 [1/6]
Checkout our new benchmark, SWE-Atlas Test Writing from @scale_AI . Its the second benchmark in the SWE-Atlas Evaluation suite. High quality test writing tasks - Agentic, Underspecified, Professional grade evaluation. Frontier models score <45% and GPT 5.4 leads the pack.
📣 New paper by my Ph.D. student @hyungjoochae How can we build a safe, scalable learning environment for web agents? Paper: arxiv.org/abs/2603.10505 We introduce VERIENV, which clones real websites into executable synthetic environments using coding agents.
@shuyanzh36 My student has a paper on this: arxiv.org/abs/2603.10505
VLMs are surprisingly good at geolocating images, but do they respect people’s expectations about location privacy? Ray’s ICLR 2026 paper digs into this.
🚨 Excited to share that our paper "Do Vision-Language Models Respect Contextual Integrity in Location Disclosure?" is accepted to #ICLR 2026! Recent work shows that VLMs excel at geolocating images, posing serious privacy risks. However, privacy is not simply about refusing to
On the hardest problems—where we want models to learn the most -- RL gives zero signal, with no correct rollouts to learn from. Ethan’s recent work (@EthanMendes3) shows how models can still learn -- by leveraging expert solutions that are normally out-of-distribution for LRMs.
It's difficult to learn with RL on hard problems: (1) they can be non-verifiable, (2) models fail to generate correct completions that can be rewarded ❌ SFT on raw human solutions hurts performance 💡 We propose DAIL to effectively learn directly from high-quality solutions
I’ll be presenting my work, “Language Models Can Self-Improve at State-Value Estimation for Better Search,” tomorrow at #NeurIPS2025. Come by Exhibit Hall C/D/E, poster #1916, from 11 AM–2 PM if you’re interested in improving agent reasoning in low-data regimes!
At #NeurIPS2025 through Sunday. Come say hi and check out our posters on: 🔒Probabilistic reasoning for text anonymity estimation: Wednesday @ 11am 🤖 Efficient, self-improving agents: Friday @ 11am
@Saikallis9012 @NoSyu @jkumarsharma998 I don't believe there was a recording of this talk, but here is a link to the slides: dropbox.com/scl/fi/c2egiwd…
What if LLMs could predict their own accuracy on a new task before running a single experiment? We introduce PRECOG, built from real papers, to study description→performance forecasting. On both static and streaming tasks, GPT-5 beats human NLP researchers and simple baselines.
What if LLMs can forecast their own scores on unseen benchmarks from just a task description? We are the first to study text description→performance prediction, giving practitioners an early read on outcomes so they can plan what to build—before paying full price 💸
🎉 Excited to see that our paper on cost-efficient data annotation for LLMs won an SAC Highlight Award! 🔗 Check out @mohit_rag18's work here: aclanthology.org/2025.acl-long.…
Check out @mohit_rag18's recent work analyzing data annotation costs associated with SFT vs. Preference Fine-Tuning.
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