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s1: Simple test-time scaling

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Test-time scaling is a promising new approach to language modeling that uses extra test-time compute to improve performance. Recently, OpenAI's o1 model showed this capability but did not publicly share its methodology, leading to many replication efforts. We seek the simplest approach to achieve test-time scaling and strong reasoning performance. First, we curate a small dataset s1K of 1,000 questions paired with reasoning traces relying on three criteria we validate through ablations: difficulty, diversity, and quality. Second, we develop budget forcing to control test-time compute by forcefully terminating the model's thinking process or lengthening it by appending "Wait" multiple times to the model's generation when it tries to end. This can lead the model to double-check its answer, often fixing incorrect reasoning steps. After supervised finetuning the Qwen2.5-32B-Instruct language model on s1K and equipping it with budget forcing, our model s1-32B exceeds o1-preview on competition math questions by up to 27% (MATH and AIME24). Further, scaling s1-32B with budget forcing allows extrapolating beyond its performance without test-time intervention: from 50% to 57% on AIME24. Our model, data, and code are open-source at https://github.com/simplescaling/s1

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  • abstract Test-time scaling is a promising new approach to language modeling that uses extra test-time compute to improve performance. Recently, OpenAI's o1 model showed this capability but did not publicly share its methodology, leading to many replication efforts. We seek the simplest approach to achieve test-time scaling and strong reasoning performance. First, we curate a small dataset s1K of 1,000 questions paired with reasoning traces relying on three criteria we validate through ablations: difficulty, diversity, and quality. Second, we develop budget forcing to control test-time compute by forcef

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GIANTS: Generative Insight Anticipation from Scientific Literature

cs.CL · 2026-04-10 · unverdicted · novelty 8.0

GIANTS-4B, trained with RL on a new 17k-example benchmark of parent-to-child paper insights, achieves 34% relative improvement over gemini-3-pro in LM-judge similarity and is rated higher-impact by a citation predictor.

When to Think Deeply: Inhibitory Deliberation for LLM Reasoning

cs.CL · 2026-06-04 · unverdicted · novelty 7.0

IDPR is a response-conditioned inhibitory deliberation method that trains a controller on fast-slow outcome pairs to decide when to override LLM fast answers, improving accuracy from 47.90% to 48.92% with slow reasoning invoked on only 8.20% of a 5,000-example math test set.

PANDO: Efficient Multimodal AI Agents via Online Skill Distillation

cs.AI · 2026-05-24 · unverdicted · novelty 7.0

PANDO introduces an online skill-distillation method with a structured library, reflection, demotion, routing, compression, and cache-aware prompting that reaches 58.3% success on 910 VisualWebArena tasks using 58-61% fewer tokens than prior methods.

Preserving Long-Tailed Expert Information in Mixture-of-Experts Tuning

cs.LG · 2026-04-24 · unverdicted · novelty 7.0

A new SFT framework for MoE models combines bias-driven sparsification with gated condenser experts to retain long-tailed expert information, outperforming DenseMixer and ESFT by over 2.5% on math reasoning and commonsense QA benchmarks.

User-Assistant Bias in LLMs

cs.CL · 2025-08-16 · unverdicted · novelty 7.0

LLMs show strong user bias in role-tagged contexts that is amplified by preference alignment and can be reduced or controlled through targeted fine-tuning and DPO.

Bayesian Social Deduction with Graph-Informed Language Models

cs.AI · 2025-06-21 · unverdicted · novelty 7.0

Hybrid Bayesian-graph LLM agent reaches competitive performance against large models and achieves 67% win rate against humans in controlled Avalon play, outperforming baselines and human teammates.

Towards Unconstrained Human-Object Interaction

cs.CV · 2026-04-15 · unverdicted · novelty 7.0

Introduces the U-HOI task and shows MLLMs plus a language-to-graph pipeline can handle human-object interactions without any predefined vocabulary at training or inference time.

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