Reinforcement Speculative Decoding for Fast Ranking

• URL: http://arxiv.org/abs/2505.20316v1
• Date: Fri, 23 May 2025 02:25:26 GMT
• Title: Reinforcement Speculative Decoding for Fast Ranking
• Abstract summary: Large Language Models (LLMs) have been widely adopted in ranking systems such as information retrieval (IR) systems and recommender systems (RSs)<n>We propose a Reinforcementive Decoding method for fast ranking inference of LLMs.
• Score: 9.584558586988953
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) have been widely adopted in ranking systems such as information retrieval (IR) systems and recommender systems (RSs). To alleviate the latency of auto-regressive decoding, some studies explore the single (first) token decoding for ranking approximation, but they suffer from severe degradation in tail positions. Although speculative decoding (SD) methods can be a remedy with verification at different positions, they face challenges in ranking systems due to their left-to-right decoding paradigm. Firstly, ranking systems require strict latency constraints, but verification rounds in SD methods remain agnostic; Secondly, SD methods usually discard listwise ranking knowledge about unaccepted items in previous rounds, hindering future multi-token prediction, especially when candidate tokens are the unaccepted items. In this paper, we propose a Reinforcement Speculative Decoding method for fast ranking inference of LLMs. To meet the ranking systems' latency requirement, we propose an up-to-down decoding paradigm that employs an agent to iteratively modify the ranking sequence under a constrained budget. Specifically, we design a ranking-tailored policy optimization, actively exploring optimal multi-round ranking modification policy verified by LLMs via reinforcement learning (RL). To better approximate the target LLM under the constrained budget, we trigger the agent fully utilizing the listwise ranking knowledge about all items verified by LLMs across different rounds in RL, enhancing the modification policy of the agent. More importantly, we demonstrate the theoretical robustness and advantages of our paradigm and implementation. Experiments on both IR and RS tasks show the effectiveness of our proposed method.

Related papers

• ResiSpec: Enhancing Multi-Candidate Speculative Sampling via Residual Distribution Shaping [79.1905186547363]
  ResiSpec is a framework that strategically reforms the proposal distribution during verification.<n>It prevents candidate obsolescence and achieves up to 1.92$times$ speedup over state-of-the-art multi-candidate methods.
  arXiv  Detail & Related papers  (2026-08-25T11:25:55Z)
• LLM-as-a-Verifier: A General-Purpose Verification Framework [74.40111651545979]
  We introduce LLM-as-a-Verifier, a general-purpose verification framework.<n>It provides fine-grained feedback for agentic tasks without requiring additional training.<n>It achieves state-of-the-art performance on several benchmarks.
  arXiv  Detail & Related papers  (2026-07-06T17:59:35Z)
• ResRank: Unifying Retrieval and Listwise Reranking via End-to-End Joint Training with Residual Passage Compression [5.223477157195793]
  "lost in the middle" phenomenon degrades ranking quality as input length grows.<n>Inference latency scales super-linearly with sequence length, rendering listwise reranking impractical for industrial deployment.<n>We present ResRank, a unified retrieval-reranking framework that fundamentally addresses both challenges.
  arXiv  Detail & Related papers  (2026-04-24T03:11:51Z)
• Metric-agnostic Learning-to-Rank via Boosting and Rank Approximation [33.0347118359824]
  We propose a new differentiable ranking loss that combines a smooth approximation to the ranking operator with the average mean square loss per query.<n>Our method outperforms the current state-of-the-art in information retrieval measures with similar efficiency.
  arXiv  Detail & Related papers  (2026-04-16T14:59:00Z)
• $\ abla$-Reasoner: LLM Reasoning via Test-Time Gradient Descent in Latent Space [71.23672814629448]
  $nabla$-Reasoner is an iterative generation framework that integrates differentiable optimization over token logits into the decoding loop.<n>$nabla$-Reasoner achieves over 20% accuracy improvement on a challenging mathematical reasoning benchmark.
  arXiv  Detail & Related papers  (2026-03-05T08:42:54Z)
• Autoregressive Ranking: Bridging the Gap Between Dual and Cross Encoders [37.16464474575651]
  We show that pointwise generative ranking with multi-token docIDs is superior to that of dual encoders.<n>We propose SToICaL - a Simple Token-Item Calibrated Loss - which can incorporate rank-aware supervision at both the item and token levels.
  arXiv  Detail & Related papers  (2026-01-09T07:16:28Z)
• Rank-GRPO: Training LLM-based Conversational Recommender Systems with Reinforcement Learning [70.6126069527741]
  ConvRec-R1 is a two-stage framework for end-to-end training of conversational recommender systems.<n>In Stage 1, we construct a behavioral-cloning dataset with a Remap-Reflect-Adjust pipeline.<n>In Stage 2, we propose Rank-GRPO, a principled extension of group relative policy optimization.
  arXiv  Detail & Related papers  (2025-10-23T02:56:00Z)
• Are LLMs Reliable Rankers? Rank Manipulation via Two-Stage Token Optimization [7.7899746437628385]
  We present Rank Anything First (RAF), a two-stage token optimization method.<n>RAF crafts concise textual perturbations to consistently promote a target item in large language models.<n>RAF generates ranking-promoting prompts token-by-token, guided by dual objectives: maximizing ranking effectiveness and preserving linguistic naturalness.
  arXiv  Detail & Related papers  (2025-10-08T07:40:40Z)
• GRAPE: Let GPRO Supervise Query Rewriting by Ranking for Retrieval [19.73916326078242]
  The CLIP model has become a cornerstone of large-scale retrieval systems by aligning text and image data in a unified embedding space.<n>To avoid costly retraining, existing methods mainly adopt query-rewriting strategies with large language models (LLMs)<n>We address this challenge with GRAPE, a plug-and-play enhancement approach that incorporates ranking signals into retrieval-guided query rewriting.
  arXiv  Detail & Related papers  (2025-09-27T15:36:59Z)
• Efficient Inference for Large Language Model-based Generative Recommendation [78.38878421030522]
  Large Language Model (LLM)-based generative recommendation has achieved notable success, yet its practical deployment is costly.<n>Applying Speculative Decoding (SD) to generative recommendation presents unique challenges due to the requirement of generating top-K items.<n>We propose an alignment framework named AtSpeed, which presents the AtSpeed-S optimization objective for top-K alignment under the strict top-K verification.
  arXiv  Detail & Related papers  (2024-10-07T16:23:36Z)
• Improving LLM Reasoning through Scaling Inference Computation with Collaborative Verification [52.095460362197336]
  Large language models (LLMs) struggle with consistent and accurate reasoning. LLMs are trained primarily on correct solutions, reducing their ability to detect and learn from errors. We propose a novel collaborative method integrating Chain-of-Thought (CoT) and Program-of-Thought (PoT) solutions for verification.
  arXiv  Detail & Related papers  (2024-10-05T05:21:48Z)
• Sifting through the Chaff: On Utilizing Execution Feedback for Ranking the Generated Code Candidates [46.74037090843497]
  Large Language Models (LLMs) are transforming the way developers approach programming by automatically generating code based on natural language descriptions. This paper puts forward RankEF, an innovative approach for code ranking that leverages execution feedback. Experiments on three code generation benchmarks demonstrate that RankEF significantly outperforms the state-of-the-art CodeRanker.
  arXiv  Detail & Related papers  (2024-08-26T01:48:57Z)
• FIRST: Faster Improved Listwise Reranking with Single Token Decoding [56.727761901751194]
  First, we introduce FIRST, a novel listwise LLM reranking approach leveraging the output logits of the first generated identifier to directly obtain a ranked ordering of the candidates. Empirical results demonstrate that FIRST accelerates inference by 50% while maintaining a robust ranking performance with gains across the BEIR benchmark. Our results show that LLM rerankers can provide a stronger distillation signal compared to cross-encoders, yielding substantial improvements in retriever recall after relevance feedback.
  arXiv  Detail & Related papers  (2024-06-21T21:27:50Z)
• Leveraging Passage Embeddings for Efficient Listwise Reranking with Large Language Models [17.420756201557957]
  We propose PE-Rank, leveraging the single passage embedding as a good context compression for efficient listwise passage reranking.<n>We introduce an inference method that dynamically constrains the decoding space to these special tokens, accelerating the decoding process.<n>Results on multiple benchmarks demonstrate that PE-Rank significantly improves efficiency in both prefilling and decoding, while maintaining competitive ranking effectiveness.
  arXiv  Detail & Related papers  (2024-06-21T03:33:51Z)
• LiPO: Listwise Preference Optimization through Learning-to-Rank [62.02782819559389]
  Policy can learn more effectively from a ranked list of plausible responses given the prompt.<n>We show that LiPO-$lambda$ can outperform DPO variants and SLiC by a clear margin on several preference alignment tasks.
  arXiv  Detail & Related papers  (2024-02-02T20:08:10Z)
• Rank-DETR for High Quality Object Detection [52.82810762221516]
  A highly performant object detector requires accurate ranking for the bounding box predictions. In this work, we introduce a simple and highly performant DETR-based object detector by proposing a series of rank-oriented designs.
  arXiv  Detail & Related papers  (2023-10-13T04:48:32Z)
• Don't Be So Sure! Boosting ASR Decoding via Confidence Relaxation [7.056222499095849]
  beam search seeks the transcript with the greatest likelihood computed using the predicted distribution. We show that recently proposed Self-Supervised Learning (SSL)-based ASR models tend to yield exceptionally confident predictions. We propose a decoding procedure that improves the performance of fine-tuned ASR models.
  arXiv  Detail & Related papers  (2022-12-27T06:42:26Z)
• Recommendation Systems with Distribution-Free Reliability Guarantees [83.80644194980042]
  We show how to return a set of items rigorously guaranteed to contain mostly good items. Our procedure endows any ranking model with rigorous finite-sample control of the false discovery rate. We evaluate our methods on the Yahoo! Learning to Rank and MSMarco datasets.
  arXiv  Detail & Related papers  (2022-07-04T17:49:25Z)

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.