Cover Image

Think, Prune, Train, Improve: Scaling Reasoning without Scaling Models

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Costello, Caia, Guo, Simon, Goldie, Anna, Mirhoseini, Azalia
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

That Chip Has Sailed: A Critique of Unfounded Skepticism Around AI for Chip Design
by: Goldie, Anna, et al.
Published: (2024)

Synthetic Data Generation & Multi-Step RL for Reasoning & Tool Use
by: Goldie, Anna, et al.
Published: (2025)

On the Role of Temperature Sampling in Test-Time Scaling
by: Wu, Yuheng, et al.
Published: (2025)

CodeMonkeys: Scaling Test-Time Compute for Software Engineering
by: Ehrlich, Ryan, et al.
Published: (2025)

ForTIFAI: Fending Off Recursive Training Induced Failure for AI Model Collapse
by: Shabgahi, Soheil Zibakhsh, et al.
Published: (2025)

Federation of Experts: Communication Efficient Distributed Inference for Large Language Models
by: Abdurrahman, Muhammad Shahir, et al.
Published: (2026)

SPRINT: Enabling Interleaved Planning and Parallelized Execution in Reasoning Models
by: Biju, Emil, et al.
Published: (2025)

Large Language Monkeys: Scaling Inference Compute with Repeated Sampling
by: Brown, Bradley, et al.
Published: (2024)

OpenJarvis: Personal AI, On Personal Devices
by: Saad-Falcon, Jon, et al.
Published: (2026)

Agent JIT Compilation for Latency-Optimizing Web Agent Planning and Scheduling
by: Winston, Caleb, et al.
Published: (2026)

CATS: Contextually-Aware Thresholding for Sparsity in Large Language Models
by: Lee, Donghyun, et al.
Published: (2024)

CHESS: Contextual Harnessing for Efficient SQL Synthesis
by: Talaei, Shayan, et al.
Published: (2024)

Hydragen: High-Throughput LLM Inference with Shared Prefixes
by: Juravsky, Jordan, et al.
Published: (2024)

KernelBench: Can LLMs Write Efficient GPU Kernels?
by: Ouyang, Anne, et al.
Published: (2025)

Reasoning-SQL: Reinforcement Learning with SQL Tailored Partial Rewards for Reasoning-Enhanced Text-to-SQL
by: Pourreza, Mohammadreza, et al.
Published: (2025)

Think Clearly: Improving Reasoning via Redundant Token Pruning
by: Choi, Daewon, et al.
Published: (2025)

Scaling Reasoning without Attention
by: Zhao, Xueliang, et al.
Published: (2025)

TEMPO: Scaling Test-time Training for Large Reasoning Models
by: Zhang, Qingyang, et al.
Published: (2026)

Think Before You Prune: Self-Reflective Structured Pruning for Reasoning Language Models
by: Wang, Ziyan, et al.
Published: (2025)

Large-Scale Dataset Pruning in Adversarial Training through Data Importance Extrapolation
by: Nieth, Björn, et al.
Published: (2024)

P$^2$ Law: Scaling Law for Post-Training After Model Pruning
by: Chen, Xiaodong, et al.
Published: (2024)

Predictive Scaling Laws for Efficient GRPO Training of Large Reasoning Models
by: Nimmaturi, Datta, et al.
Published: (2025)

Scaling Reward Modeling without Human Supervision
by: Fan, Jingxuan, et al.
Published: (2026)

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

Nemotron-CrossThink: Scaling Self-Learning beyond Math Reasoning
by: Akter, Syeda Nahida, et al.
Published: (2025)

Reconstruct the Pruned Model without Any Retraining
by: Wang, Pingjie, et al.
Published: (2024)

Z-Pruner: Post-Training Pruning of Large Language Models for Efficiency without Retraining
by: Bhuiyan, Samiul Basir, et al.
Published: (2025)

Unity is Power: Semi-Asynchronous Collaborative Training of Large-Scale Models with Structured Pruning in Resource-Limited Clients
by: Li, Yan, et al.
Published: (2024)

Quantized Reasoning Models Think They Need to Think Longer, but They Do Not
by: Lotfi, Sanae, et al.
Published: (2026)

How Do Large Language Monkeys Get Their Power (Laws)?
by: Schaeffer, Rylan, et al.
Published: (2025)

Asynchronous Reasoning: Training-Free Interactive Thinking LLMs
by: Yakushev, George, et al.
Published: (2025)

Thinking in Frames: How Visual Context and Test-Time Scaling Empower Video Reasoning
by: Li, Chengzu, et al.
Published: (2026)

TaTToo: Tool-Grounded Thinking PRM for Test-Time Scaling in Tabular Reasoning
by: Zou, Jiaru, et al.
Published: (2025)

Input-Time Scaling: Adding Noise and Irrelevance into Less-Is-More Drastically Improves Reasoning Performance and Efficiency
by: Huang, Rapheal, et al.
Published: (2025)

Lightweight Dataset Pruning without Full Training via Example Difficulty and Prediction Uncertainty
by: Cho, Yeseul, et al.
Published: (2025)

Reasoning-as-Logic-Units: Scaling Test-Time Reasoning in Large Language Models Through Logic Unit Alignment
by: Li, Cheryl, et al.
Published: (2025)

Improving LLM Reasoning through Scaling Inference Computation with Collaborative Verification
by: Liang, Zhenwen, et al.
Published: (2024)

Improving Line Search Methods for Large Scale Neural Network Training
by: Kenneweg, Philip, et al.
Published: (2024)

Scaling Reasoning Hop Exposes Weaknesses: Demystifying and Improving Hop Generalization in Large Language Models
by: Li, Zhaoyi, et al.
Published: (2026)

WISCA: A Lightweight Model Transition Method to Improve LLM Training via Weight Scaling
by: Li, Jiacheng, et al.
Published: (2025)