mlsanityData to 7 Oct 2026

Cascade Reward Sampling for Efficient Decoding-Time Alignment

arXiv 2406.16306 ↗First posted 24 Jun 2024cs.CL cs.LG stat.MLcited by 38

On the map: LLM alignment / RLHF / preference optimisation → LLM preference optimization methods

Abstract (arXiv)

Aligning large language models (LLMs) with human preferences is essential for their applications. Recently, decoding-time alignment has emerged as an effective plug-and-play technique that avoids fine-tuning model parameters. This approach retains the general utility of pretrained LLMs but often suffers from significant inefficiencies during decoding, primarily due to wasted token generation and excessive reward evaluations. To address these challenges, we introduce Cascade Reward Sampling (CARDS) to resolve both efficiency bottlenecks in decoding-time alignment. Specifically, we develop a segment-level rejection sampling algorithm that minimizes redundant computations of both LLMs and reward models (RMs). Central to CARDS is an uncertainty-based segmentation mechanism, which ensures the accuracy of RMs evaluations on incomplete segments. Furthermore, we provide a detailed analysis of reward scores on segments to elucidate the improved alignment performance. Experimental results demonstrate that CARDS significantly improves decoding efficiency, alignment quality, and general utility compared to existing decoding-time alignment methods, achieving approximately a 70% reduction in decoding time and over 90% win-ties in utility and safety benchmarks.

Key facts. Cited by 38 papers in the map (data through 7 Oct 2026). Placed in LLM preference optimization methods, one of 10 directions in LLM alignment / RLHF / preference optimisation. Builds on 62 of its 88 references that are in the map. First posted on arXiv 24 Jun 2024.

Builds on

62 of 88 references.

Closest ancestors · 16

Same direction, newest first

  1. 2024Hybrid Alignment Training for Large Language Models21 Jun
  2. 20243D-Properties: Identifying Challenges in DPO and Charting a Path Forward11 Jun
  3. 2024Transfer Q Star: Principled Decoding for LLM Alignment30 May
  4. 2024Weak-to-Strong Search: Align Large Language Models via Searching over Small Language Models29 May
  5. 2024SimPO: Simple Preference Optimization with a Reference-Free Reward23 May
  6. 2024Disperse-Then-Merge: Pushing the Limits of Instruction Tuning via Alignment Tax Reduction22 May
  7. 2024Rewards-in-Context: Multi-objective Alignment of Foundation Models with Dynamic Preference Adjustment15 Feb
  8. 2024RS-DPO: A Hybrid Rejection Sampling and Direct Preference Optimization Method for Alignment of Large Language Models15 Feb
  9. 2024DeAL: Decoding-time Alignment for Large Language Models5 Feb
  10. 2024Decoding-time Realignment of Language Models5 Feb
  11. 2024ARGS: Alignment as Reward-Guided Search23 Jan
  12. 2023The Unlocking Spell on Base LLMs: Rethinking Alignment via In-Context Learning4 Dec
4 more
  1. 2023RAIN: Your Language Models Can Align Themselves without Finetuning13 Sep
  2. 2023Statistical Rejection Sampling Improves Preference Optimization13 Sep
  3. 2023Aligning Language Models with Preferences through f-divergence Minimization16 Feb
  4. 2022Training language models to follow instructions with human feedback4 Mar

Other references · 46

  1. 2023GPT-4 Technical Reportcited by 18,132
  2. 2017Proximal Policy Optimization Algorithmscited by 14,441
  3. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  4. 2024DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Modelscited by 8,851
  5. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 7,916
  6. 2017Deep reinforcement learning from human preferencescited by 3,846
  7. 2023Tree of Thoughts: Deliberate Problem Solving with Large Language Modelscited by 3,155
  8. 2022Constitutional AI: Harmlessness from AI Feedbackcited by 2,950
  9. 2023Universal and Transferable Adversarial Attacks on Aligned Language Modelscited by 2,914
  10. 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,579
  11. 2020Learning to summarize from human feedbackcited by 2,562
  12. 2022Emergent Abilities of Large Language Modelscited by 2,483
34 more
  1. 2016A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networkscited by 2,362
  2. 2022GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformerscited by 1,838
  3. 2021WebGPT: Browser-assisted question-answering with human feedbackcited by 1,722
  4. 2020RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Modelscited by 1,235
  5. 2023Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To!cited by 1,106
  6. 2022Solving math word problems with process- and outcome-based feedbackcited by 871
  7. 2024Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluatorscited by 865
  8. 2023BeaverTails: Towards Improved Safety Alignment of LLM via a Human-Preference Datasetcited by 828
  9. 2019Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learningcited by 750
  10. 2018Evidential Deep Learning to Quantify Classification Uncertaintycited by 728
  11. 2023RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignmentcited by 672
  12. 2023Safe RLHF: Safe Reinforcement Learning from Human Feedbackcited by 630
  13. 2018Predictive Uncertainty Estimation via Prior Networkscited by 555
  14. 2023Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervisioncited by 445
  15. 2021FUDGE: Controlled Text Generation With Future Discriminatorscited by 374
  16. 2023Toxicity in ChatGPT: Analyzing Persona-assigned Language Modelscited by 356
  17. 2023Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-Constraintcited by 346
  18. 2021PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-trainingcited by 298
  19. 2024Unlocking Efficiency in Large Language Model Inference: A Comprehensive Survey of Speculative Decodingcited by 266
  20. 2023How is ChatGPT's behavior changing over time?cited by 265
  21. 2023Challenges and Applications of Large Language Modelscited by 251
  22. 2023Chain of Hindsight Aligns Language Models with Feedbackcited by 147
  23. 2023HelpSteer: Multi-attribute Helpfulness Dataset for SteerLMcited by 140
  24. 2023Controlled Decoding from Language Modelscited by 133
  25. 2024What is in Your Safe Data? Identifying Benign Data that Breaks Safetycited by 114
  26. 2024Weak-to-Strong Jailbreaking on Large Language Modelscited by 100
  27. 2024Probabilistic Inference in Language Models via Twisted Sequential Monte Carlocited by 85
  28. 2022On Reinforcement Learning and Distribution Matching for Fine-Tuning Language Models with no Catastrophic Forgettingcited by 80
  29. 2023Reward-Augmented Decoding: Efficient Controlled Text Generation With a Unidirectional Reward Modelcited by 79
  30. 2024"My Answer is C": First-Token Probabilities Do Not Match Text Answers in Instruction-Tuned Language Modelscited by 66
  31. 2021Adversarial Robustness with Semi-Infinite Constrained Learningcited by 39
  32. 2024Improving Large Language Models via Fine-grained Reinforcement Learning with Minimum Editing Constraintcited by 39
  33. 2024Creativity Has Left the Chat: The Price of Debiasing Language Modelscited by 29
  34. 2021Sampling from Discrete Energy-Based Models with Quality/Efficiency Trade-offscited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 23; 2026: 11.

38 papers.

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  1. 2025Is Best-of-N the Best of Them? Coverage, Scaling, and Optimality in Inference-Time Alignmentcited by 91
  2. 2025Sampling-Efficient Test-Time Scaling: Self-Estimating the Best-of-N Sampling in Early Decodingcited by 62
  3. 2024TreeBoN: Enhancing Inference-Time Alignment with Speculative Tree-Search and Best-of-N Samplingcited by 61
  4. 2024ETA: Evaluating Then Aligning Safety of Vision Language Models at Inference Timecited by 57
  5. 2024GenARM: Reward Guided Generation with Autoregressive Reward Model for Test-time Alignmentcited by 54
  6. 2025Test-Time Preference Optimization: On-the-Fly Alignment via Iterative Textual Feedbackcited by 41
  7. 2025PARM: Multi-Objective Test-Time Alignment via Preference-Aware Autoregressive Reward Modelcited by 27
  8. 2025A Survey on Personalized and Pluralistic Preference Alignment in Large Language Modelscited by 25
  9. 2025Sailing by the Stars: A Survey on Reward Models and Learning Strategies for Learning from Rewardscited by 14
  10. 2025Segmenting Text and Learning Their Rewards for Improved RLHF in Language Modelcited by 12
  11. 2025From Outcomes to Processes: Guiding PRM Learning from ORM for Inference-Time Alignmentcited by 9
  12. 2025Well Begun is Half Done: Low-resource Preference Alignment by Weak-to-Strong Decodingcited by 7
26 more
  1. 2024GAS: Generative Auto-bidding with Post-training Searchcited by 6
  2. 2025Towards Cost-Effective Reward Guided Text Generationcited by 5
  3. 2025Simultaneous Multi-objective Alignment Across Verifiable and Non-verifiable Rewardscited by 5
  4. 2025Structure-R1: Dynamically Leveraging Structural Knowledge in LLM Reasoning through Reinforcement Learningcited by 5
  5. 2025Reward-Shifted Speculative Sampling Is An Efficient Test-Time Weak-to-Strong Alignercited by 4
  6. 2025The Reward Model Selection Crisis in Personalized Alignmentcited by 3
  7. 2026UniARM: Towards a Unified Autoregressive Reward Model for Multi-Objective Test-Time Alignmentcited by 3
  8. 2025Cheems: A Practical Guidance for Building and Evaluating Chinese Reward Models from Scratchcited by 2
  9. 2025Tokenized Bandit for LLM Decoding and Alignmentcited by 2
  10. 2025A Survey on Training-free Alignment of Large Language Modelscited by 2
  11. 2025Controlling Multimodal LLMs via Reward-guided Decodingcited by 2
  12. 2025Mitigating Overthinking through Reasoning Shapingcited by 2
  13. 2026Learning Adaptive LLM Decodingcited by 2
  14. 2025Aligning Frozen LLMs by Reinforcement Learning: An Iterative Reweight-then-Optimize Approachcited by 1
  15. 2025Adaptive Blockwise Search: Inference-Time Alignment for Large Language Modelscited by 1
  16. 2025STARS: Synchronous Token Alignment for Robust Supervision in Large Language Modelscited by 1
  17. 2026LLMdoctor: Token-Level Flow-Guided Preference Optimization for Efficient Test-Time Alignment of Large Language Modelscited by 1
  18. 2026GuardAlign: Test-time Safety Alignment in Multimodal Large Language Modelscited by 1
  19. 2026TARo: Token-level Adaptive Routing for LLM Test-time Alignmentcited by 1
  20. 2025Test-time reward-guided alignment of language models by importance sampling on pre-logit spacecited by 0
  21. 2026Selective Safety Steering via Value-Filtered Decodingcited by 0
  22. 2026Gradient-Guided Reward Optimization for Inference-time Alignmentcited by 0
  23. 2026Best-of-Better-N: Generating Pre-Aligned Responses with In-Context Learningcited by 0
  24. 2026Swiss-Knife: A Framework for Reconfigurable Externalised Multi-Objective Alignment at Decode Timecited by 0
  25. 2026Rethinking Personalized Generation: Test-Time Alignment via Factorized Ranking Modelscited by 0
  26. 2026FastGuide: Accelerating Reward Guidance for Diffusion Large Language Modelscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Safe Inference-Time Alignment via Lagrangian Reward Augmentation
  2. 2024Decoding-Time Language Model Alignment with Multiple Objectives
  3. 2025Alignment-Aware Decoding
  4. 2024RMB: Comprehensively Benchmarking Reward Models in LLM Alignment
  5. 2025Energy-Based Reward Models for Robust Language Model Alignment
  6. 2024Fast Best-of-N Decoding via Speculative Rejection
  7. 2024Regularized Best-of-N Sampling with Minimum Bayes Risk Objective for Language Model Alignment
  8. 2025Towards Bridging the Reward-Generation Gap in Direct Alignment Algorithms