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DecisionRank: On-Policy Distillation for Decision-Model Rerankers

Paper recorded by Signals 4 on 2026-10-30 in cs.CL. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-30 on arXiv · recorded by Signals 4 on 2026-11-02

Category: cs.CL · 自然语言处理 · first seen 2026-11-02

Abstract

Decision models target structured tasks by directly predicting choices or scores, avoiding autoregressive decoding latency. However, transferring a generative LLM's ranking knowledge to a decision model remains challenging: the teacher expresses preferences through a ranked list of document ID sequences, while the student determines the ranking using scores from the first-token position. To bridge

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#44 most recent of 628 cs.CL papers we have recorded · ↑ newer: Flide: Accelerating Decision Inference with Exact State Forking · ↓ older: RangeSWA: Training-Free Length Extrapolation for Continuous Diffusion
Cite this page: DecisionRank: On-Policy Distillation for Decision-Model Rerankers: the #44 most recent of 628 cs.CL papers we have recorded (as of 2026-10-30). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/decisionrank-on-policy-distillation-for-decision-model-rerankers.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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