Saved in:
| Main Authors: | Hsu, Hsiang, Lei, Eric, Chen, Chun-Fu |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.06797 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Is Best-of-N the Best of Them? Coverage, Scaling, and Optimality in Inference-Time Alignment
by: Huang, Audrey, et al.
Published: (2025)
by: Huang, Audrey, et al.
Published: (2025)
Revisiting the (Sub)Optimality of Best-of-N for Inference-Time Alignment
by: Sriraman, Ved, et al.
Published: (2026)
by: Sriraman, Ved, et al.
Published: (2026)
Does Privacy Always Harm Fairness? Data-Dependent Trade-offs via Chernoff Information Neural Estimation
by: Nichani, Arjun, et al.
Published: (2026)
by: Nichani, Arjun, et al.
Published: (2026)
Mitigating Preference Hacking in Policy Optimization with Pessimism
by: Gupta, Dhawal, et al.
Published: (2025)
by: Gupta, Dhawal, et al.
Published: (2025)
The Unseen Threat: Residual Knowledge in Machine Unlearning under Perturbed Samples
by: Hsu, Hsiang, et al.
Published: (2026)
by: Hsu, Hsiang, et al.
Published: (2026)
Optimism Stabilizes Thompson Sampling for Adaptive Inference
by: Yan, Shunxing, et al.
Published: (2026)
by: Yan, Shunxing, et al.
Published: (2026)
Diffusion Model-Augmented Behavioral Cloning
by: Chen, Shang-Fu, et al.
Published: (2023)
by: Chen, Shang-Fu, et al.
Published: (2023)
DROP: Distributional and Regular Optimism and Pessimism for Reinforcement Learning
by: Kobayashi, Taisuke
Published: (2024)
by: Kobayashi, Taisuke
Published: (2024)
TreeBoN: Enhancing Inference-Time Alignment with Speculative Tree-Search and Best-of-N Sampling
by: Qiu, Jiahao, et al.
Published: (2024)
by: Qiu, Jiahao, et al.
Published: (2024)
Unified PAC-Bayesian Study of Pessimism for Offline Policy Learning with Regularized Importance Sampling
by: Aouali, Imad, et al.
Published: (2024)
by: Aouali, Imad, et al.
Published: (2024)
Learnable Chernoff Baselines for Inference-Time Alignment
by: Madhow, Sunil, et al.
Published: (2026)
by: Madhow, Sunil, et al.
Published: (2026)
TailedTS: Benchmark Dataset for Heavy-Tailed Time Series Prediction and Periodicity Quantification
by: Chen, Xinyu, et al.
Published: (2026)
by: Chen, Xinyu, et al.
Published: (2026)
Harnesses for Inference-Time Alignment over Execution Trajectories
by: Wang, Boyuan, et al.
Published: (2026)
by: Wang, Boyuan, et al.
Published: (2026)
Dynamic Search for Inference-Time Alignment in Diffusion Models
by: Li, Xiner, et al.
Published: (2025)
by: Li, Xiner, et al.
Published: (2025)
Variational Best-of-N Alignment
by: Amini, Afra, et al.
Published: (2024)
by: Amini, Afra, et al.
Published: (2024)
Bridging Past and Future: Distribution-Aware Alignment for Time Series Forecasting
by: Hu, Yifan, et al.
Published: (2025)
by: Hu, Yifan, et al.
Published: (2025)
Inference-Time Alignment of Diffusion Models with Direct Noise Optimization
by: Tang, Zhiwei, et al.
Published: (2024)
by: Tang, Zhiwei, et al.
Published: (2024)
Reward Shaping for Inference-Time Alignment: A Stackelberg Game Perspective
by: Wang, Haichuan, et al.
Published: (2026)
by: Wang, Haichuan, et al.
Published: (2026)
Inference-Time Alignment Control for Diffusion Models with Reinforcement Learning Guidance
by: Jin, Luozhijie, et al.
Published: (2025)
by: Jin, Luozhijie, et al.
Published: (2025)
Quality at the Tail of Machine Learning Inference
by: Yang, Zhengxin, et al.
Published: (2022)
by: Yang, Zhengxin, et al.
Published: (2022)
Alpamayo-R1: Bridging Reasoning and Action Prediction for Generalizable Autonomous Driving in the Long Tail
by: NVIDIA, et al.
Published: (2025)
by: NVIDIA, et al.
Published: (2025)
IterIS: Iterative Inference-Solving Alignment for LoRA Merging
by: Chen, Hongxu, et al.
Published: (2024)
by: Chen, Hongxu, et al.
Published: (2024)
Space Alignment Matters: The Missing Piece for Inducing Neural Collapse in Long-Tailed Learning
by: Wang, Jinping, et al.
Published: (2025)
by: Wang, Jinping, et al.
Published: (2025)
Echo: Decoupling Inference and Training for Large-Scale RL Alignment on Heterogeneous Swarms
by: Xiao, Jie, et al.
Published: (2025)
by: Xiao, Jie, et al.
Published: (2025)
Sample, Scrutinize and Scale: Effective Inference-Time Search by Scaling Verification
by: Zhao, Eric, et al.
Published: (2025)
by: Zhao, Eric, et al.
Published: (2025)
AdaBoN: Adaptive Best-of-N Alignment
by: Raman, Vinod, et al.
Published: (2025)
by: Raman, Vinod, et al.
Published: (2025)
Beyond the Lower Bound: Bridging Regret Minimization and Best Arm Identification in Lexicographic Bandits
by: Xue, Bo, et al.
Published: (2025)
by: Xue, Bo, et al.
Published: (2025)
Pessimism in the Face of Confounders: Provably Efficient Offline Reinforcement Learning in Partially Observable Markov Decision Processes
by: Lu, Miao, et al.
Published: (2022)
by: Lu, Miao, et al.
Published: (2022)
Frequency-Masked Embedding Inference: A Non-Contrastive Approach for Time Series Representation Learning
by: Fu, En, et al.
Published: (2024)
by: Fu, En, et al.
Published: (2024)
PITA: Preference-Guided Inference-Time Alignment for LLM Post-Training
by: Bobbili, Sarat Chandra, et al.
Published: (2025)
by: Bobbili, Sarat Chandra, et al.
Published: (2025)
Time-o1: Time-Series Forecasting Needs Transformed Label Alignment
by: Wang, Hao, et al.
Published: (2025)
by: Wang, Hao, et al.
Published: (2025)
Boosting Cross-problem Generalization in Diffusion-Based Neural Combinatorial Solver via Inference Time Adaptation
by: Lei, Haoyu, et al.
Published: (2025)
by: Lei, Haoyu, et al.
Published: (2025)
Best-of-$\infty$ -- Asymptotic Performance of Test-Time LLM Ensembling
by: Komiyama, Junpei, et al.
Published: (2025)
by: Komiyama, Junpei, et al.
Published: (2025)
STEB: In Search of the Best Evaluation Approach for Synthetic Time Series
by: Stenger, Michael, et al.
Published: (2025)
by: Stenger, Michael, et al.
Published: (2025)
Bridging the Gap Between Preference Alignment and Machine Unlearning
by: Feng, Xiaohua, et al.
Published: (2025)
by: Feng, Xiaohua, et al.
Published: (2025)
Understanding the Impact of Differentially Private Training on Memorization of Long-Tailed Data
by: Zhang, Jiaming, et al.
Published: (2026)
by: Zhang, Jiaming, et al.
Published: (2026)
STEER: Inference-Time Risk Control via Constrained Quality-Diversity Search
by: Yang, Eric, et al.
Published: (2026)
by: Yang, Eric, et al.
Published: (2026)
TimeBridge: Better Diffusion Prior Design with Bridge Models for Time Series Generation
by: Park, Jinseong, et al.
Published: (2024)
by: Park, Jinseong, et al.
Published: (2024)
Temper and Tilt Lead to SLOP: Reward Hacking Mitigation with Inference-Time Alignment
by: Wang, Ye, et al.
Published: (2026)
by: Wang, Ye, et al.
Published: (2026)
Disentangled Safety Adapters Enable Efficient Guardrails and Flexible Inference-Time Alignment
by: Krishna, Kundan, et al.
Published: (2025)
by: Krishna, Kundan, et al.
Published: (2025)
Similar Items
-
Is Best-of-N the Best of Them? Coverage, Scaling, and Optimality in Inference-Time Alignment
by: Huang, Audrey, et al.
Published: (2025) -
Revisiting the (Sub)Optimality of Best-of-N for Inference-Time Alignment
by: Sriraman, Ved, et al.
Published: (2026) -
Does Privacy Always Harm Fairness? Data-Dependent Trade-offs via Chernoff Information Neural Estimation
by: Nichani, Arjun, et al.
Published: (2026) -
Mitigating Preference Hacking in Policy Optimization with Pessimism
by: Gupta, Dhawal, et al.
Published: (2025) -
The Unseen Threat: Residual Knowledge in Machine Unlearning under Perturbed Samples
by: Hsu, Hsiang, et al.
Published: (2026)