Token-Level Off-Policy Learning for Faithful Generation Under Distribution Shift

• URL: http://arxiv.org/abs/2607.17524v1
• Date: Mon, 20 Jul 2026 03:57:22 GMT
• Title: Token-Level Off-Policy Learning for Faithful Generation Under Distribution Shift
• Abstract summary: Token-Level Off-Policy Labeling (TOPL) is an off-policy training paradigm that reframes post-training as a token-level correctness prediction task.<n>We show TOPL achieves strong out-of-distribution generalization across 11 datasets against a diverse set of sequence-level and token-level baselines.
• Score: 26.752668789722765
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose Token-Level Off-Policy Labeling (TOPL), an off-policy training paradigm that reframes post-training as a token-level correctness prediction task. Our key intuition is that by training the model to distinguish good and bad tokens in a response, we naturally guide the model towards generating good tokens, while avoiding the pitfalls that come with directly training the model to generate off-policy tokens. Experiments on document summarization tasks show that TOPL achieves strong out-of-distribution generalization across 11 datasets against a diverse set of sequence-level and token-level baselines. We further demonstrate that TOPL transfers effectively to machine translation, suggesting that its benefits generalize across different faithful generation tasks. Through ablation studies, we confirm that our token-level learning signal is critical to good performance; sequence-level analogues do not confer similar benefits. Finally, we show that TOPL induces interpretable model updates: the LoRA adapters learned through TOPL function as linear classification heads and steering vectors.

Related papers

• PriFT: Prior-Support Guided Supervised Fine-Tuning [74.65198014829393]
  Supervised fine-tuning (SFT) is an efficient approach for downstream task adaptation.<n>SFT fits fixed demonstrations token by token, including targets poorly aligned with the model's pretrained distribution.<n>We propose PriFT, which derives token weights from a frozen pretrained reference to obtain a stable reweighting signal unaffected by fine-tuning.
  arXiv  Detail & Related papers  (2026-06-08T12:14:06Z)
• PRPO: Perception-Reinforced Policy Optimization via Token-Level Dynamic Advantage Reshaping [5.89473045822308]
  Reinforcement Learning with Verifiable Rewards (RLVR) has become an effective paradigm for improving the reasoning capability of Large Vision-Language Models (LVLMs)<n>Existing RLVR methods rely on trajectory-level outcome rewards, which assign identical learning signals across all generated tokens.<n>We propose Perception-Reinforced Policy Optimization (PRPO), a token-level reinforcement learning framework that explicitly identifies and reinforces pivotal perceptual tokens.
  arXiv  Detail & Related papers  (2026-06-07T16:06:29Z)
• Unlearning What Matters: Token-Level Attribution for Precise Language Model Unlearning [5.454773103061359]
  TokenUnlearn is a token-level attribution framework that identifies and selectively targets critical tokens.<n>Our approach combines knowledge-aware signals via masking, and entropy-aware signals to yield importance scores for precise token selection.
  arXiv  Detail & Related papers  (2026-05-01T02:59:03Z)
• Rubrics to Tokens: Bridging Response-level Rubrics and Token-level Rewards in Instruction Following Tasks [30.108876878365496]
  Reinforcement Learning (RL) has emerged as a promising approach for aligning Large Language Models with complex, open-domain instruction following tasks.<n>Existing methods predominantly rely on response-level rewards, introducing severe reward sparsity and reward ambiguity problems.<n>We propose a novel rubric-based RL framework that bridges coarse response-level scores and fine-grained token-level credit assignment.
  arXiv  Detail & Related papers  (2026-04-03T07:02:57Z)
• Principled RL for Diffusion LLMs Emerges from a Sequence-Level Perspective [85.06838178922791]
  Reinforcement Learning (RL) has proven highly effective for autoregressive language models.<n>But adapting these methods to diffusion large language models (dLLMs) presents fundamental challenges.<n>We propose a principled RL framework that treats entire sequence generation as a single action and uses the ELBO as a tractable sequence-level likelihood proxy.
  arXiv  Detail & Related papers  (2025-12-03T13:05:32Z)
• ssToken: Self-modulated and Semantic-aware Token Selection for LLM Fine-tuning [51.133569963553576]
  ssToken is a Self-modulated and Semantic-aware Token Selection approach.<n>We show that both self-modulated selection and semantic-aware selection alone outperform full-data fine-tuning.
  arXiv  Detail & Related papers  (2025-10-21T03:21:04Z)
• ASPO: Asymmetric Importance Sampling Policy Optimization [31.38346888572171]
  The Importance Sampling (IS) ratios of positive-advantage tokens are mismatched, leading to unbalanced token weighting for positive and negative tokens.<n>This mismatch suppresses the update of low-probability tokens while over-amplifying already high-probability ones.<n>We propose Asymmetric Importance Sampling Policy Optimization (ASPO), which uses a simple yet effective strategy that flips the IS ratios of positive-advantage tokens.
  arXiv  Detail & Related papers  (2025-10-07T15:54:24Z)
• IGD: Token Decisiveness Modeling via Information Gain in LLMs for Personalized Recommendation [79.22388408461458]
  We introduce an Information Gain-based Decisiveness-aware Token handling (IGD) strategy that integrates token decisiveness into both tuning and decoding.<n>IGD consistently improves recommendation accuracy, achieving significant gains on widely used ranking metrics compared to strong baselines.
  arXiv  Detail & Related papers  (2025-06-16T08:28:19Z)
• Aligning Instruction Tuning with Pre-training [61.50161961371844]
  We propose Aligning Instruction Tuning with Pre-training (AITP) to align instruction tuning with pre-training distributions.<n>We show consistent performance improvements with AITP on three fully open large language models (LLMs) across eight benchmarks.
  arXiv  Detail & Related papers  (2025-01-16T08:27:40Z)
• Critical Tokens Matter: Token-Level Contrastive Estimation Enhances LLM's Reasoning Capability [53.51560766150442]
  Critical tokens are elements within reasoning trajectories that significantly influence incorrect outcomes.<n>We present a novel framework for identifying these tokens through rollout sampling.<n>We show that identifying and replacing critical tokens significantly improves model accuracy.
  arXiv  Detail & Related papers  (2024-11-29T18:58:22Z)
• On the Transferability of Pre-trained Language Models: A Study from Artificial Datasets [74.11825654535895]
  Pre-training language models (LMs) on large-scale unlabeled text data makes the model much easier to achieve exceptional downstream performance. We study what specific traits in the pre-training data, other than the semantics, make a pre-trained LM superior to their counterparts trained from scratch on downstream tasks.
  arXiv  Detail & Related papers  (2021-09-08T10:39:57Z)

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.