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SpecReason: Fast and Accurate Inference-Time Compute via Speculative Reasoning
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abstract
Recent advances in inference-time compute have significantly improved performance on complex tasks by generating long chains of thought (CoTs) using Large Reasoning Models (LRMs). However, this improved accuracy comes at the cost of high inference latency due to the length of generated reasoning sequences and the autoregressive nature of decoding. Our key insight in tackling these overheads is that LRM inference, and the reasoning that it embeds, is highly tolerant of approximations: complex tasks are typically broken down into simpler steps, each of which brings utility based on the semantic insight it provides for downstream steps rather than the exact tokens it generates. Accordingly, we introduce SpecReason, a system that automatically accelerates LRM inference by using a lightweight model to (speculatively) carry out simpler intermediate reasoning steps and reserving the costly base model only to assess (and potentially correct) the speculated outputs. Importantly, SpecReason's focus on exploiting the semantic flexibility of thinking tokens in preserving final-answer accuracy is complementary to prior speculation techniques, most notably speculative decoding, which demands token-level equivalence at each step. Across a variety of reasoning benchmarks, SpecReason achieves $1.4-3.0\times$ speedup over vanilla LRM inference while improving accuracy by $0.4-9.0\%$. Compared to speculative decoding without SpecReason, their combination yields an additional $8.8-58.0\%$ latency reduction. We open-source SpecReason at https://github.com/ruipeterpan/specreason.
Forward citations
Cited by 8 Pith papers
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SpecEyes: Accelerating Agentic Multimodal LLMs via Speculative Perception and Planning
A small tool-free MLLM plus answer-separability gating bypasses agentic tool loops for many queries, yielding 1.1–3.35× speedup with preserved or higher accuracy.
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Scaling Up, Speeding Up: A Benchmark of Speculative Decoding for Efficient LLM Test-Time Scaling
N-gram based speculative decoding methods, especially SAM and hybrid SAM[EAGLE-3], achieve strong speedups in LLM test-time scaling by exploiting repetitive reasoning patterns.
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POSS: Position Specialist Generates Better Draft for Speculative Decoding
Using position-specialized draft layers instead of one single draft model improves later-token acceptance in speculative decoding, yielding modest speedups on Llama-3-8B and Llama-2-13B.
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Done Is Better than Perfect: Unlocking Efficient Reasoning by Structured Multi-Turn Decomposition
Structuring a reasoning model's chain of thought into multi-turn answer steps and optimizing with reinforcement learning reduces token usage and latency by up to roughly 70 percent with only small accuracy losses.
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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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TrimR: Verifier-based Training-Free Thinking Compression for Efficient Test-Time Scaling
TrimR uses a lightweight verifier to detect redundant thoughts in reasoning models and truncates them, cutting runtime by up to 70% without retraining.
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MixReasoning: Switching Modes to Think
A reasoning model can cut token usage by roughly a third to a half without losing accuracy if it switches to brief output on low-uncertainty steps and expands only at high-uncertainty forks.
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Fast Quiet-STaR: Thinking Without Thought Tokens
Fast Quiet-STaR uses a curriculum to compress Quiet-STaR's token-level thoughts and an RL stage to remove them entirely, improving accuracy on four benchmarks at the same or lower inference cost.
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