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Showing papers from California Institute of Technology (Caltech) Show all papers

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NeurIPS 2026U Illinois Urbana-ChampaignCalifornia Institute of TechnoloU Illinois at Urbana-ChampaignOffline RL

Beyond Pessimism: Offline Learning in KL-regularized Games

A pessimism-free offline algorithm for KL-regularized games achieves O(1/n) sample complexity via equilibrium stability and smooth best responses.

Yuheng Zhang, Claire Chen, Nan Jiang

Atlanta Poster Session 4, Thu, Dec 10, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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11/20 AI panelreviewers recommend it

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AI panel: 11 of 20 reviewers recommend it
lenient 2/5
medium 6/10
strict 3/5
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NeurIPS 2026U Virginia, CharlottesvilleCiscoCalifornia Institute of TechnoloPurdueU VirginiaMeta, transfer & few-shot learning

Predicting Plasticity in Deep Continual Learning: A Theoretical Perspective

Existing plasticity diagnostics fail to predict trainability, but optimization readiness, combining gradient strength and reliability, lower-bounds optimization gain and predicts plasticity more reliably.

Jiuqi Wang, Jayanth Srinivasa, Claire Chen, Shuze D Liu and 2 more

Atlanta Poster Session 4, Thu, Dec 10, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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16/20 AI panelreviewers recommend it

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AI panel: 16 of 20 reviewers recommend it
lenient 4/5
medium 10/10
strict 2/5
76%Highly rated
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NeurIPS 2026PurdueCalifornia Institute of TechnoloCalifornia Institute of TechnoloU Illinois Urbana-ChampaignCaltech, Asari AIRL for LLMs

Instructing LLMs to Negotiate using Reinforcement Learning with Verifiable Rewards

RLVR trains a 30B LLM buyer via verifiable economic rewards to negotiate, revealing four-phase strategic evolution and outperforming much larger frontier models in surplus extraction.

Shuze D Liu, Claire Chen, Jiabao S Xiao, Lei Lei and 3 more

Atlanta Poster Session 5, Fri, Dec 11, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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10/20 AI panelreviewers recommend it

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AI panel: 10 of 20 reviewers recommend it
lenient 5/5
medium 3/10
strict 2/5