Arbitrage: Efficient Reasoning via Advantage-Aware Speculation

• URL: http://arxiv.org/abs/2512.05033v2
• Date: Tue, 09 Dec 2025 18:32:43 GMT
• Title: Arbitrage: Efficient Reasoning via Advantage-Aware Speculation
• Authors: Monishwaran Maheswaran, Rishabh Tiwari, Yuezhou Hu, Kerem Dilmen, Coleman Hooper, Haocheng Xi, Nicholas Lee, Mehrdad Farajtabar, Michael W. Mahoney, Kurt Keutzer, Amir Gholami,
• Abstract summary: Speculative Decoding accelerates inference by employing a fast but inaccurate draft model to autoregressively propose tokens.<n>But due to unnecessary rejections caused by token mismatches in semantically equivalent steps, traditional token-level Speculative Decoding struggles in reasoning tasks.<n>We propose Arbitrage, a novel step-level speculative generation framework that routes generation dynamically based on the relative advantage between draft and target models.
• Score: 71.45710345765528
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modern Large Language Models achieve impressive reasoning capabilities with long Chain of Thoughts, but they incur substantial computational cost during inference, and this motivates techniques to improve the performance-cost ratio. Among these techniques, Speculative Decoding accelerates inference by employing a fast but inaccurate draft model to autoregressively propose tokens, which are then verified in parallel by a more capable target model. However, due to unnecessary rejections caused by token mismatches in semantically equivalent steps, traditional token-level Speculative Decoding struggles in reasoning tasks. Although recent works have shifted to step-level semantic verification, which improve efficiency by accepting or rejecting entire reasoning steps, existing step-level methods still regenerate many rejected steps with little improvement, wasting valuable target compute. To address this challenge, we propose Arbitrage, a novel step-level speculative generation framework that routes generation dynamically based on the relative advantage between draft and target models. Instead of applying a fixed acceptance threshold, Arbitrage uses a lightweight router trained to predict when the target model is likely to produce a meaningfully better step. This routing approximates an ideal Arbitrage Oracle that always chooses the higher-quality step, achieving near-optimal efficiency-accuracy trade-offs. Across multiple mathematical reasoning benchmarks, Arbitrage consistently surpasses prior step-level Speculative Decoding baselines, reducing inference latency by up to $\sim2\times$ at matched accuracy.

Related papers

• ConfSpec: Efficient Step-Level Speculative Reasoning via Confidence-Gated Verification [0.2578242050187029]
  Step-level speculative reasoning aims to mitigate this cost, yet existing approaches face a long-standing trade-off.<n>We propose ConfSpec, a confidence-gated cascaded verification framework that resolves this trade-off.
  arXiv  Detail & Related papers  (2026-01-28T05:58:05Z)
• GlimpRouter: Efficient Collaborative Inference by Glimpsing One Token of Thoughts [10.808072653940263]
  Collaborative inference offers a promising solution by selectively allocating work between lightweight and large models.<n>We propose a novel perspective on step-wise collaboration: the difficulty of a reasoning step can be inferred from its very first token.<n>Glimp employs a lightweight model to generate only the first token of each reasoning step and routes the step to a larger model only when the initial token entropy exceeds a threshold.
  arXiv  Detail & Related papers  (2026-01-08T16:58:07Z)
• PROMISE: Process Reward Models Unlock Test-Time Scaling Laws in Generative Recommendations [52.67948063133533]
  Generative Recommendation has emerged as a promising paradigm, reformulating recommendation as a sequence-to-sequence generation task over hierarchical Semantic IDs.<n>Existing methods suffer from a critical issue we term Semantic Drift, where errors in early, high-level tokens irreversibly divert the generation trajectory into irrelevant semantic subspaces.<n>We propose Promise, a novel framework that integrates dense, step-by-step verification into generative models.
  arXiv  Detail & Related papers  (2026-01-08T07:38:46Z)
• Accelerate Speculative Decoding with Sparse Computation in Verification [49.74839681322316]
  Speculative decoding accelerates autoregressive language model inference by verifying multiple draft tokens in parallel.<n>Existing sparsification methods are designed primarily for standard token-by-token autoregressive decoding.<n>We propose a sparse verification framework that jointly sparsifies attention, FFN, and MoE components during the verification stage to reduce the dominant computation cost.
  arXiv  Detail & Related papers  (2025-12-26T07:53:41Z)
• Training-Free Loosely Speculative Decoding: Accepting Semantically Correct Drafts Beyond Exact Match [21.810129153556044]
  Training-Free Loosely Speculative Decoding (FLy) is a novel method that loosens the rigid verification criterion.<n>We show that FLy preserves more than 99% of the target model's accuracy while achieving an average 2.81x speedup.
  arXiv  Detail & Related papers  (2025-11-28T08:23:30Z)
• In-Token Rationality Optimization: Towards Accurate and Concise LLM Reasoning via Self-Feedback [38.915062716409686]
  InTRO is a new framework that enables both token-level exploration and self-feedback for accurate and concise reasoning.<n>InTRO consistently outperforms other baselines, raising solution accuracy by up to 20% relative to the base model.<n>Its chains of thought are notably more concise, exhibiting reduced verbosity.
  arXiv  Detail & Related papers  (2025-11-13T01:47:06Z)
• Adaptive Test-Time Reasoning via Reward-Guided Dual-Phase Search [62.1546099504045]
  We propose a dual-phase test-time scaling framework that separates reasoning into planning and execution.<n>Specifically, we decompose reasoning trajectories and develop reward models for each phase, enabling the search to explore and prune plans and executions separately.<n> Experiments on both mathematical reasoning and code generation benchmarks demonstrate that our approach consistently improves accuracy while reducing computation redundant.
  arXiv  Detail & Related papers  (2025-09-29T19:27:23Z)
• Fast Thinking for Large Language Models [67.7238685892317]
  We introduce Latent Codebooks for Fast Thinking, a framework that uses concise CoT sketches only during training to learn a codebook of discrete strategy priors.<n>At inference, the model conditions on a handful of continuous thinking switches distilled from the codebook in a single pass, enabling strategy-level guidance without producing explicit reasoning tokens.
  arXiv  Detail & Related papers  (2025-09-28T04:19:48Z)
• READER: Retrieval-Assisted Drafter for Efficient LLM Inference [0.0386965802948046]
  Autoregressive Language Models instantiate a factorized likelihood over token sequences, yet their strictly sequential decoding process imposes an intrinsic lower bound on latency inference.<n>This bottleneck has emerged as a central obstacle to the scalable deployment of large-scale generative models.<n>We present READER, a speculative decoding framework that bypasses the training of the auxiliary draft model.
  arXiv  Detail & Related papers  (2025-08-12T16:47:48Z)
• R-Stitch: Dynamic Trajectory Stitching for Efficient Reasoning [80.104336426172]
  Chain-of-thought (CoT) enhances problem-solving ability of large language models.<n>CoT incurs substantial inference cost due to long autoregressive trajectories.<n>We introduce R-Stitch, a training-free hybrid decoding framework.
  arXiv  Detail & Related papers  (2025-07-23T08:14:36Z)
• FTP: A Fine-grained Token-wise Pruner for Large Language Models via Token Routing [17.01412432658081]
  Large language models (LLMs) have demonstrated superior performance across various tasks by adhering to scaling laws.<n>We propose a fine-grained token-wise pruning approach for the LLMs, which presents a learnable router to adaptively identify the less important tokens.<n>Our approach achieves state-of-the-art (SOTA) pruning results, surpassing other existing pruning methods.
  arXiv  Detail & Related papers  (2024-12-16T07:09:46Z)
• COrAL: Order-Agnostic Language Modeling for Efficient Iterative Refinement [80.18490952057125]
  Iterative refinement has emerged as an effective paradigm for enhancing the capabilities of large language models (LLMs) on complex tasks. We propose Context-Wise Order-Agnostic Language Modeling (COrAL) to overcome these challenges. Our approach models multiple token dependencies within manageable context windows, enabling the model to perform iterative refinement internally.
  arXiv  Detail & Related papers  (2024-10-12T23:56:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.