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Judge Decoding: Faster Speculative Sampling Requires Going Beyond Model Alignment
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Judge Decoding: Faster Speculative Sampling Requires Going Beyond Model Alignment
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The performance of large language models (LLMs) is closely linked to their underlying size, leading to ever-growing networks and hence slower inference. Speculative decoding has been proposed as a technique to accelerate autoregressive generation, leveraging a fast draft model to propose candidate tokens, which are then verified in parallel based on their likelihood under the target model. While this approach guarantees to reproduce the target output, it incurs a substantial penalty: many high-quality draft tokens are rejected, even when they represent objectively valid continuations. Indeed, we show that even powerful draft models such as GPT-4o, as well as human text cannot achieve high acceptance rates under the standard verification scheme. This severely limits the speedup potential of current speculative decoding methods, as an early rejection becomes overwhelmingly likely when solely relying on alignment of draft and target. We thus ask the following question: Can we adapt verification to recognize correct, but non-aligned replies? To this end, we draw inspiration from the LLM-as-a-judge framework, which demonstrated that LLMs are able to rate answers in a versatile way. We carefully design a dataset to elicit the same capability in the target model by training a compact module on top of the embeddings to produce ``judgements" of the current continuation. We showcase our strategy on the Llama-3.1 family, where our 8b/405B-Judge achieves a speedup of 9x over Llama-405B, while maintaining its quality on a large range of benchmarks. These benefits remain present even in optimized inference frameworks, where our method reaches up to 141 tokens/s for 8B/70B-Judge and 129 tokens/s for 8B/405B on 2 and 8 H100s respectively.
Forward citations
Cited by 10 Pith papers
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Bastion: Budget-Aware Speculative Decoding with Tree-structured Block Diffusion Drafting
BASTION is a budget-aware speculative decoding framework with adaptive tree-structured block diffusion drafting that reports up to 6.61x speedup and 39% improvement over block-diffusion baselines.
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WISV: Wireless-Informed Semantic Verification for Distributed Speculative Decoding in Device-Edge LLM Inference
WISV uses a channel-aware semantic acceptance policy on hidden representations to boost accepted sequence length by up to 60.8% and cut interaction rounds by 37.3% in distributed speculative decoding, with under 1% ac...
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Adversarial Prompts for Acceptance Collapse in Speculative Decoding
ADSD shows that a short adversarial suffix appended to a prompt can collapse the token-acceptance rate in speculative decoding, increasing latency by 62.3% on GSM8K while preserving answer accuracy.
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Unlocking Parallelism in Autoregressive Language Models via Speculative Decoding with Progressive Tree Drafting
PTD accelerates autoregressive LLM decoding up to ~2× by guiding the target model to explore multiple coherent draft paths via a progressive, pruned tree in a single forward pass.
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Beyond the Target: From Imitation to Collaboration in Speculative Decoding
CoSpec uses RL to arbitrate between draft and target tokens in speculative decoding, maintaining speedups while exceeding target-only performance.
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Training-Free Loosely Speculative Decoding: Accepting Semantically Correct Drafts Beyond Exact Match
FLy is a training-free method that speeds up LLM generation by accepting semantically correct but non-exact draft tokens via an entropy gate and deferred verification window.
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Speculative Coupled Decoding for Training-Free Lossless Acceleration of Autoregressive Visual Generation
Speculative Coupled Decoding stabilizes draft sampling in Speculative Jacobi Decoding via an information-theoretic coupling step, delivering up to 4.2x image and 13.6x video speedups with no quality loss or training.
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SpecFed: Accelerating Federated LLM Inference with Speculative Decoding and Compressed Transmission
SpecFed accelerates federated LLM inference via speculative decoding for parallel processing and top-K compression with server-side reconstruction, achieving high fidelity with reduced communication overhead.
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LogitSpec: Accelerating Retrieval-based Speculative Decoding via Next Next Token Speculation
LogitSpec accelerates retrieval-based speculative decoding by speculating the next-next token from the last logit and retrieving relevant references for both next and next-next tokens, reporting up to 2.61x speedup an...
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SpecFed: Accelerating Federated LLM Inference with Speculative Decoding and Compressed Transmission
Speculative decoding plus top-K compressed transmission with two reconstruction strategies cuts federated LLM communication while bounding reconstruction, aggregation, and acceptance-rate bias.
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