:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Chen, Feng, Raventos, Allan, Cheng, Nan, Ganguli, Surya, Druckmann, Shaul
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence
Online Access:	https://arxiv.org/abs/2502.07154
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Deriving Neural Scaling Laws from the statistics of natural language
by: Cagnetta, Francesco, et al.
Published: (2026)

Unlocking LLM Creativity in Science through Analogical Reasoning
by: Shen, Andrew, et al.
Published: (2026)

Get rich quick: exact solutions reveal how unbalanced initializations promote rapid feature learning
by: Kunin, Daniel, et al.
Published: (2024)

Informed Correctors for Discrete Diffusion Models
by: Zhao, Yixiu, et al.
Published: (2024)

A Cross-Modal Approach to Silent Speech with LLM-Enhanced Recognition
by: Benster, Tyler, et al.
Published: (2024)

Stochastic Collapse: How Gradient Noise Attracts SGD Dynamics Towards Simpler Subnetworks
by: Chen, Feng, et al.
Published: (2023)

Fooling LLM graders into giving better grades through neural activity guided adversarial prompting
by: Yamamura, Atsushi, et al.
Published: (2024)

Rethinking Optimal Verification Granularity for Compute-Efficient Test-Time Scaling
by: Chen, Hao Mark, et al.
Published: (2025)

Multi-Agent Reasoning Improves Compute Efficiency: Pareto-Optimal Test-Time Scaling
by: Wunderlich, Florian Valentin, et al.
Published: (2026)

BitCal-TTS: Bit-Calibrated Test-Time Scaling for Quantized Reasoning Models
by: Patarlapalli, Sai Babu, et al.
Published: (2026)

An analytic theory of creativity in convolutional diffusion models
by: Kamb, Mason, et al.
Published: (2024)

Examining False Positives under Inference Scaling for Mathematical Reasoning
by: Wang, Yu, et al.
Published: (2025)

MathScale: Scaling Instruction Tuning for Mathematical Reasoning
by: Tang, Zhengyang, et al.
Published: (2024)

MathSE: Improving Multimodal Mathematical Reasoning via Self-Evolving Iterative Reflection and Reward-Guided Fine-Tuning
by: Chen, Jinhao, et al.
Published: (2025)

TMAS: Scaling Test-Time Compute via Multi-Agent Synergy
by: Wu, George, et al.
Published: (2026)

Optimizing Test-Time Compute via Meta Reinforcement Fine-Tuning
by: Qu, Yuxiao, et al.
Published: (2025)

TFGN: Task-Free, Replay-Free Continual Pre-Training Without Catastrophic Forgetting at LLM Scale
by: Ganguli, Anurup
Published: (2026)

Maximizing Confidence Alone Improves Reasoning
by: Prabhudesai, Mihir, et al.
Published: (2025)

CoRefine: Confidence-Guided Self-Refinement for Adaptive Test-Time Compute
by: Jin, Chen, et al.
Published: (2026)

From Kepler to Newton: Inductive Biases Guide Learned World Models in Transformers
by: Liu, Ziming, et al.
Published: (2026)

Rethinking Reward Models for Multi-Domain Test-Time Scaling
by: Lee, Dong Bok, et al.
Published: (2025)

Log-Augmented Generation: Scaling Test-Time Reasoning with Reusable Computation
by: Chen, Peter Baile, et al.
Published: (2025)

NeuroProlog: Multi-Task Fine-Tuning for Neurosymbolic Mathematical Reasoning via the Cocktail Effect
by: Zunjare, Pratibha, et al.
Published: (2026)

Less is More: Improving LLM Reasoning with Minimal Test-Time Intervention
by: Yang, Zhen, et al.
Published: (2025)

Towards Thinking-Optimal Scaling of Test-Time Compute for LLM Reasoning
by: Yang, Wenkai, et al.
Published: (2025)

Forest-of-Thought: Scaling Test-Time Compute for Enhancing LLM Reasoning
by: Bi, Zhenni, et al.
Published: (2024)

ReThinker: Scientific Reasoning by Rethinking with Guided Reflection and Confidence Control
by: Tang, Zhentao, et al.
Published: (2026)

Contrastive Concept-Tree Search for LLM-Assisted Algorithm Discovery
by: Leleu, Timothee, et al.
Published: (2026)

Inverse Scaling in Test-Time Compute
by: Gema, Aryo Pradipta, et al.
Published: (2025)

Rethinking Supervised Fine-Tuning: Emphasizing Key Answer Tokens for Improved LLM Accuracy
by: Shi, Xiaofeng, et al.
Published: (2025)

Rethinking the Role of Prompting Strategies in LLM Test-Time Scaling: A Perspective of Probability Theory
by: Liu, Yexiang, et al.
Published: (2025)

Guided by Gut: Efficient Test-Time Scaling with Reinforced Intrinsic Confidence
by: Ghasemabadi, Amirhosein, et al.
Published: (2025)

BrowseConf: Confidence-Guided Test-Time Scaling for Web Agents
by: Ou, Litu, et al.
Published: (2025)

Agentic Test-Time Scaling for WebAgents
by: Lee, Nicholas, et al.
Published: (2026)

Efficiently Learning at Test-Time: Active Fine-Tuning of LLMs
by: Hübotter, Jonas, et al.
Published: (2024)

On-Policy Supervised Fine-Tuning for Efficient Reasoning
by: Zhao, Anhao, et al.
Published: (2026)

Thinking vs. Doing: Agents that Reason by Scaling Test-Time Interaction
by: Shen, Junhong, et al.
Published: (2025)

Mitigating Strategy-Selection Bias in Reasoning for More Effective Test-Time Scaling
by: Wu, Zongqian, et al.
Published: (2025)

MatryoshkaThinking: Recursive Test-Time Scaling Enables Efficient Reasoning
by: Chen, Hongwei, et al.
Published: (2025)

GRIP: In-Parameter Graph Reasoning through Fine-Tuning Large Language Models
by: Feng, Jiarui, et al.
Published: (2025)