Budget-aware Test-time Scaling via Discriminative Verification
Fuente:
arXiv
Saved in:
| Main Authors: | Montgomery, Kyle, Tan, Sijun, Chen, Yuqi, Zhuang, Siyuan, Zhang, Tianjun, Popa, Raluca Ada, Wang, Chenguang |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
JudgeBench: A Benchmark for Evaluating LLM-based Judges
by: Tan, Sijun, et al.
Published: (2024)
by: Tan, Sijun, et al.
Published: (2024)
LLoCO: Learning Long Contexts Offline
by: Tan, Sijun, et al.
Published: (2024)
by: Tan, Sijun, et al.
Published: (2024)
Predicting Task Performance with Context-aware Scaling Laws
by: Montgomery, Kyle, et al.
Published: (2025)
by: Montgomery, Kyle, et al.
Published: (2025)
Auditing Black-Box LLM APIs with a Rank-Based Uniformity Test
by: Zhu, Xiaoyuan, et al.
Published: (2025)
by: Zhu, Xiaoyuan, et al.
Published: (2025)
Agent Instructs Large Language Models to be General Zero-Shot Reasoners
by: Crispino, Nicholas, et al.
Published: (2023)
by: Crispino, Nicholas, et al.
Published: (2023)
Re-Tuning: Overcoming the Compositionality Limits of Large Language Models with Recursive Tuning
by: Pasewark, Eric, et al.
Published: (2024)
by: Pasewark, Eric, et al.
Published: (2024)
Aligning Tree-Search Policies with Fixed Token Budgets in Test-Time Scaling of LLMs
by: Miyamoto, Sora, et al.
Published: (2026)
by: Miyamoto, Sora, et al.
Published: (2026)
GoEX: Perspectives and Designs Towards a Runtime for Autonomous LLM Applications
by: Patil, Shishir G., et al.
Published: (2024)
by: Patil, Shishir G., et al.
Published: (2024)
Safe: Enhancing Mathematical Reasoning in Large Language Models via Retrospective Step-aware Formal Verification
by: Liu, Chengwu, et al.
Published: (2025)
by: Liu, Chengwu, et al.
Published: (2025)
Self-Trained Verification for Training- and Test-Time Self-Improvement
by: Wu, Chen Henry, et al.
Published: (2026)
by: Wu, Chen Henry, et al.
Published: (2026)
Trust but Verify! A Survey on Verification Design for Test-time Scaling
by: Venktesh, V, et al.
Published: (2025)
by: Venktesh, V, et al.
Published: (2025)
Efficient Test-Time Scaling via Self-Calibration
by: Huang, Chengsong, et al.
Published: (2025)
by: Huang, Chengsong, et al.
Published: (2025)
Knapsack RL: Unlocking Exploration of LLMs via Optimizing Budget Allocation
by: Li, Ziniu, et al.
Published: (2025)
by: Li, Ziniu, et al.
Published: (2025)
HybridNorm: Towards Stable and Efficient Transformer Training via Hybrid Normalization
by: Zhuo, Zhijian, et al.
Published: (2025)
by: Zhuo, Zhijian, et al.
Published: (2025)
T1: Tool-integrated Verification for Test-time Compute Scaling in Small Language Models
by: Kang, Minki, et al.
Published: (2025)
by: Kang, Minki, et al.
Published: (2025)
Optimizing Anytime Reasoning via Budget Relative Policy Optimization
by: Qi, Penghui, et al.
Published: (2025)
by: Qi, Penghui, et al.
Published: (2025)
MetaScale: Test-Time Scaling with Evolving Meta-Thoughts
by: Liu, Qin, et al.
Published: (2025)
by: Liu, Qin, et al.
Published: (2025)
Token-Budget-Aware LLM Reasoning
by: Han, Tingxu, et al.
Published: (2024)
by: Han, Tingxu, et al.
Published: (2024)
Mitigating Reversal Curse in Large Language Models via Semantic-aware Permutation Training
by: Guo, Qingyan, et al.
Published: (2024)
by: Guo, Qingyan, et al.
Published: (2024)
Iterative Deepening Sampling as Efficient Test-Time Scaling
by: Chen, Weizhe, et al.
Published: (2025)
by: Chen, Weizhe, et al.
Published: (2025)
Mode-Conditioning Unlocks Superior Test-Time Scaling
by: Wu, Chen Henry, et al.
Published: (2025)
by: Wu, Chen Henry, et al.
Published: (2025)
PassNet: Scaling Large Language Models for Graph Compiler Pass Generation
by: Liu, Yiqun, et al.
Published: (2026)
by: Liu, Yiqun, et al.
Published: (2026)
Interpretable Discriminative Text Representations via Agreement and Label Disentanglement
by: Wang, Tong, et al.
Published: (2026)
by: Wang, Tong, et al.
Published: (2026)
PerPO: Perceptual Preference Optimization via Discriminative Rewarding
by: Zhu, Zining, et al.
Published: (2025)
by: Zhu, Zining, et al.
Published: (2025)
BAGEN: Are LLM Agents Budget-Aware?
by: Lin, Yuxiang, et al.
Published: (2026)
by: Lin, Yuxiang, et al.
Published: (2026)
OptScale: Probabilistic Optimality for Inference-time Scaling
by: Wang, Youkang, et al.
Published: (2025)
by: Wang, Youkang, et al.
Published: (2025)
PTPP-Aware Adaptation Scaling Laws: Predicting Domain-Adaptation Performance at Unseen Pre-Training Budgets
by: Goffinet, Etienne, et al.
Published: (2025)
by: Goffinet, Etienne, 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)
by: Liu, Yexiang, et al.
Published: (2025)
SETS: Leveraging Self-Verification and Self-Correction for Improved Test-Time Scaling
by: Chen, Jiefeng, et al.
Published: (2025)
by: Chen, Jiefeng, et al.
Published: (2025)
LLM CHESS: Benchmarking Reasoning and Instruction-Following in LLMs through Chess
by: Kolasani, Sai, et al.
Published: (2025)
by: Kolasani, Sai, et al.
Published: (2025)
Duel-Evolve: Reward-Free Test-Time Scaling via LLM Self-Preferences
by: Karlekar, Sweta, et al.
Published: (2026)
by: Karlekar, Sweta, et al.
Published: (2026)
Quokka: Accelerating Program Verification with LLMs via Invariant Synthesis
by: Wei, Anjiang, et al.
Published: (2025)
by: Wei, Anjiang, et al.
Published: (2025)
Mid-Think: Training-Free Intermediate-Budget Reasoning via Token-Level Triggers
by: Yang, Wang, et al.
Published: (2026)
by: Yang, Wang, et al.
Published: (2026)
Recursive Introspection: Teaching Language Model Agents How to Self-Improve
by: Qu, Yuxiao, et al.
Published: (2024)
by: Qu, Yuxiao, et al.
Published: (2024)
Scaling over Scaling: Exploring Test-Time Scaling Plateau in Large Reasoning Models
by: Wang, Jian, et al.
Published: (2025)
by: Wang, Jian, et al.
Published: (2025)
Generalizing Test-time Compute-optimal Scaling as an Optimizable Graph
by: Wang, Fali, et al.
Published: (2025)
by: Wang, Fali, et al.
Published: (2025)
Provable Scaling Laws for the Test-Time Compute of Large Language Models
by: Chen, Yanxi, et al.
Published: (2024)
by: Chen, Yanxi, et al.
Published: (2024)
LLMs on a Budget? Say HOLA
by: Siddiqui, Zohaib Hasan, et al.
Published: (2025)
by: Siddiqui, Zohaib Hasan, et al.
Published: (2025)
Revisiting the Test-Time Scaling of o1-like Models: Do they Truly Possess Test-Time Scaling Capabilities?
by: Zeng, Zhiyuan, et al.
Published: (2025)
by: Zeng, Zhiyuan, et al.
Published: (2025)
Log-Augmented Generation: Scaling Test-Time Reasoning with Reusable Computation
by: Chen, Peter Baile, et al.
Published: (2025)
by: Chen, Peter Baile, et al.
Published: (2025)
Similar Items
-
JudgeBench: A Benchmark for Evaluating LLM-based Judges
by: Tan, Sijun, et al.
Published: (2024) -
LLoCO: Learning Long Contexts Offline
by: Tan, Sijun, et al.
Published: (2024) -
Predicting Task Performance with Context-aware Scaling Laws
by: Montgomery, Kyle, et al.
Published: (2025) -
Auditing Black-Box LLM APIs with a Rank-Based Uniformity Test
by: Zhu, Xiaoyuan, et al.
Published: (2025) -
Agent Instructs Large Language Models to be General Zero-Shot Reasoners
by: Crispino, Nicholas, et al.
Published: (2023)