Good Papers

Showing papers from UCLA Computer Science Department, University of California, Los Angeles Show all papers

57%Worth a look
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RAHF: Reward-Amplified Human Feedback for Closed-Loop Policy Fine-Tuning

Haoyuan Cai, Seth Zhao, Jason Zhang, Bolei Zhou

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

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AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
57%Worth a look
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L$^2$EAP: Supercharging LLMs for Formal Mathematics with Agentic Frameworks

Po-Nien Kung, Linfeng Song, Dawsen Hwang, Jinsung Yoon and 9 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
69%Highly rated
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Demystifying Numerical Errors in LLM Inference: Achieving Reproducible Inference for Mission-Critical Tasks with HEAL

Zhenting Zhu, Lucas Thai, Shan Yu, Yicheng Liu and 4 more

Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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3/20 AI panelreviewers recommend it

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AI panel: 3 of 20 reviewers recommend it
lenient 2/5
medium 1/10
strict 0/5
89%Must read
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Inertia-1: An Open Exploration of Wearable Motion Foundation Models

Inertia-1 explores wearable motion foundation models via 18.2M hours of accelerometer data, yielding state-of-the-art recipes and open design principles for diverse sensing tasks.

Zongzhe Xu, Aakarsh Anand, Sarah Jiang, Chuntung Zhuang and 3 more

Atlanta Poster Session 5, Fri, Dec 11, 10:00 AM–1:00 PM, Hall C1 · Published 2026 · ▲ 1 on Hugging Face · Code ★ 35

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16/20 AI panelreviewers recommend it

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AI panel: 16 of 20 reviewers recommend it
lenient 5/5
medium 8/10
strict 3/5
76%Highly rated
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Beyond What Seems Necessary: Hidden Gains from Scaling Training-Time Reasoning Length under Outcome Supervision

Under outcome-only supervision, scaling training-time reasoning length improves OOD performance after ID saturation via stronger inductive biases and reduced shortcut reliance.

Yihao Xue, Allan Zhang, Jianhao Huang, Amit Sahai and 1 more

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

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AI panel: 10 of 20 reviewers recommend it
lenient 4/5
medium 5/10
strict 1/5
83%Must read
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CM2: Reinforcement Learning with Checklist Rewards for Multi-Turn and Multi-Step Agentic Tool Use

CM2 replaces verifiable outcome rewards with checklist rewards for multi-turn tool-use RL, improving 8B models by 8, 12 points on agent benchmarks using simulated environments.

Zhen Zhang, Kaiqiang Song, Sean Wang, Yebowen Hu and 10 more

Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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13/20 AI panelreviewers recommend it

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AI panel: 13 of 20 reviewers recommend it
lenient 5/5
medium 8/10
strict 0/5