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57%Worth a look
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Do Thinking Tokens Help with Safety?

Narutatsu Ri, Abhishek Panigrahi, Sanjeev Arora

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
45%Niche pick
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Learning Rate Transfer in Normalized Transformers

Boris Shigida, Boris Hanin, Andrey Gromov

Atlanta Poster Session 2, Wed, Dec 9, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
72%Highly rated
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Self-Supervised Goal-Reaching Results in Multi-Agent Cooperation and Exploration

Self-supervised goal-reaching enables multi-agent cooperation and exploration via sparse feedback, outperforming alternatives and discovering nontrivial coordination without explicit mechanisms.

Chirayu Nimonkar, Shlok Shah, Catherine Ji, Benjamin Eysenbach

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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8/20 AI panelreviewers recommend it

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AI panel: 8 of 20 reviewers recommend it
lenient 4/5
medium 3/10
strict 1/5
80%Must read
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Unifying Goal-Conditioned RL and Unsupervised Skill Learning via Control-Maximization

Unifying GCRL and MISL as control maximization reveals formulation-specific bounds linking diverse pretraining skills to downstream goal sensitivity.

Alireza Modirshanechi, Benjamin Eysenbach, Peter Dayan, Eric Schulz

Paris Poster Session 2, Wed, Dec 9, 5:00 PM–7:00 PM, Paris Poster Hall · Published 2026

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12/20 AI panelreviewers recommend it

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AI panel: 12 of 20 reviewers recommend it
lenient 2/5
medium 7/10
strict 3/5
88%Must read
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Rethinking On-Policy Self-Distillation for Thinking Models

Privileged self-distillation degrades thinking models by suppressing reasoning forks and self-correction tokens, reducing long-rollout accuracy by up to 17%.

Simran Kaur, Narutatsu Ri, Yinghui He, Liam Fowl and 1 more

Atlanta Poster Session 2, Wed, Dec 9, 4:30 PM–7:30 PM, Hall C1 · Published 2026 · ▲ 2 on Hugging Face

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15/20 AI panelreviewers recommend it

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AI panel: 15 of 20 reviewers recommend it
lenient 3/5
medium 9/10
strict 3/5
88%Must read
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LeAct: Learning to Reason from Expert Actions

LeAct recovers expert reasoning chains from actions alone to train reasoning models; it reaches near-optimal expert performance across games and robotics while improving on direct imitation.

Ziran Yang, Chengshuai Shi, Raj Ghugare, Benjamin Eysenbach and 2 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: 15 of 20 reviewers recommend it
lenient 5/5
medium 8/10
strict 2/5
70%Highly rated
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Hyperparameter Transfer for Dense Associative Memories

Derives explicit hyperparameter transfer rules for Dense Associative Memories and validates them against large-scale training.

Roi Holtzman, Dmitry Krotov, Boris Hanin

Paris Poster Session 5, Fri, Dec 11, 11:30 AM–1:30 PM, Paris Poster Hall · Published 2026

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4/20 AI panelreviewers recommend it

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AI panel: 4 of 20 reviewers recommend it
lenient 0/5
medium 4/10
strict 0/5