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Luke S. Zettlemoyer

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#artificial intelligence Preprint Sep 2026

Context Language Models

Context Language Models are introduced, language models that natively manage their own context and can be steered with natural-language instructions evolved through a standard skill-optimization loop, improving held-out accuracy by up to 35.9 points on a context-management task while reducing compute.

Ru-Lin Shao, Shannon Zejiang Shen, J. Yin et al. · 0 citations
#natural language process... Preprint Sep 2026

When Agents Slow Down: Understanding LLM Agents'Test-Time Strategies via Elo-per-token Analysis

This work defines the scaling inflection point as the per-session budget where marginal Elo gains match the independent-sampling reference, and proposes Elo-per-token analysis, which tracks the best solution found at each token budget and uses a Bradley-Terry model to aggregate within-task orderings into Elo ratings ac...

Kai-Yuan Liu, Qiu-Yang Mang, Bo-Fei Peng et al. · 3 citations
#machine learning Preprint Sep 2026

Data Scarcity and Model Sparsity: Mixtures-of-Experts Overfit More to Repeated Data

This work addresses the adverse interactions between sparsity and data repetition, and presents evidence for the core mechanisms of overfitting and its potential remediation, and suggests promising avenues for future methods to reduce over-specialization in model parameters by disrupting memorization patterns.

Atindra Jha, Margaret Li, J. Leskovec et al. · 1 citation
Preprint Aug 2026

SPADE: Self-Play in Adaptive Synthetic Executable Environments

SPADE (Self-Play in Adaptive Synthetic Executable Executable Environments), a self-play RL framework in which a single LLM plays two roles: an Environment Designer that writes complete, long-horizon training environments as executable code with an OpenAI Gym-style reset()/step() interface, and a Reasoning Agent that le...

Bo Liu, Simon Yu, Yiding Jiang et al. · 7 citations
#natural language process... Preprint Sep 2026

Knowledge Distillation During Mid-Training Favors Reasoning over Factual Recall

Switch Distillation is proposed, a simple mid-training objective that distills on tokens where the teacher is confident, using teacher predictive entropy as a lightweight routing signal, and otherwise falls back to cross-entropy, which consistently outperforms existing distillation objectives across teacher sizes.

Jacqueline He, Howard Yen, S. Li et al. · 0 citations
Preprint Aug 2026

Prefix Sliding for efficient test-time scaling

Prefix Sliding is proposed, which discards tokens during reasoning that are not part of the prefix or the window of the last few thousand tokens, allowing for efficient long-horizon test-time scaling.

Niklas Muennighoff, Zhengyuan Wang, Zeya Chen et al. · 1 citation · ⚡1
#artificial intelligence Preprint Aug 2026

SPADE: Self-Play in Adaptive Synthetic Executable Environments

SPADE (Self-Play in Adaptive Synthetic Executable Executable Environments), a self-play RL framework in which a single LLM plays two roles: an Environment Designer that writes complete, long-horizon training environments as executable code with an OpenAI Gym-style reset()/step() interface, and a Reasoning Agent that le...

Bo Liu, Simon Yu, Yiding Jiang et al. · 1 citation

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