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Fast Best-of-N Decoding via Speculative Rejection
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The safe and effective deployment of Large Language Models (LLMs) involves a critical step called alignment, which ensures that the model's responses are in accordance with human preferences. Prevalent alignment techniques, such as DPO, PPO and their variants, align LLMs by changing the pre-trained model weights during a phase called post-training. While predominant, these post-training methods add substantial complexity before LLMs can be deployed. Inference-time alignment methods avoid the complex post-training step and instead bias the generation towards responses that are aligned with human preferences. The best-known inference-time alignment method, called Best-of-N, is as effective as the state-of-the-art post-training procedures. Unfortunately, Best-of-N requires vastly more resources at inference time than standard decoding strategies, which makes it computationally not viable. In this work, we introduce Speculative Rejection, a computationally-viable inference-time alignment algorithm. It generates high-scoring responses according to a given reward model, like Best-of-N does, while being between 16 to 32 times more computationally efficient.
Forward citations
Cited by 8 Pith papers
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Best-of-N through the Smoothing Lens: KL Divergence and Regret Analysis
Smoothed Best-of-N has finite-sample KL and regret bounds under imperfect reward models, and tuning its temperature can make its regret bound beat hard Best-of-N in the overoptimization regime.
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How Far Are We from Optimal Reasoning Efficiency?
The authors define a reasoning efficiency frontier and a gap metric (REG), then train models with REO-RL to shrink the gap by at least 50% with only small accuracy losses.
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What makes Reasoning Models Different? Follow the Reasoning Leader for Efficient Decoding
FoReaL-Decoding lets a strong reasoning model generate the first few tokens of each sentence and a weaker model complete the sentence, cutting theoretical FLOPs by 30-55% while retaining 86-100% of accuracy on four ma...
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VeriThinker: Learning to Verify Makes Reasoning Model Efficient
VeriThinker shows that fine-tuning a reasoning model only on a solution-verification task reduces chain-of-thought length on MATH500 and AIME by 20-45% while preserving or slightly improving accuracy.
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Can Past Experience Accelerate LLM Reasoning?
LLMs can cut reasoning compute by up to 56% on similar questions when memory of past correct solutions is combined with adaptive early stopping, though the benchmark is small and synthetic.
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PATS: Process-Level Adaptive Thinking Mode Switching
PATS adapts the number of beam-search candidates per reasoning step using process reward model scores, improving accuracy-efficiency tradeoffs on math benchmarks.
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PREMISE: Scalable and Strategic Prompt Optimization for Efficient Mathematical Reasoning in Large Models
The paper reports that a concise optimized prompt reduces reasoning tokens by up to 87.5% while roughly preserving accuracy on GSM8K, SVAMP, and MATH-500.
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Wait, We Don't Need to "Wait"! Removing Thinking Tokens Improves Reasoning Efficiency
Suppressing "Wait"-like reflection tokens at decode time reduces reasoning token counts by 27-51% across five R1-style model families, with mixed accuracy effects.
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