Writing-Zero: Bridge the Gap Between Non-verifiable Tasks and Verifiable Rewards

• URL: http://arxiv.org/abs/2506.00103v2
• Date: Wed, 11 Jun 2025 14:56:19 GMT
• Title: Writing-Zero: Bridge the Gap Between Non-verifiable Tasks and Verifiable Rewards
• Authors: Ruipeng Jia, Yunyi Yang, Yongbo Gai, Kai Luo, Shihao Huang, Jianhe Lin, Xiaoxi Jiang, Guanjun Jiang,
• Abstract summary: We propose a unified RLVR-based training paradigm that bridges the gap between non-verifiable tasks and verifiable rewards.<n>We introduce a writing-principle-based pairwise Generative Reward Model (GenRM) and a novel Bootstrapped Relative Policy Optimization (BRPO) algorithm.<n>Our approach empowers LLMs to develop robust writing capabilities without supervised fine-tuning.
• Score: 11.149294285483782
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reinforcement learning with verifiable rewards (RLVR) has enabled large language models (LLMs) to achieve remarkable breakthroughs in reasoning tasks with objective ground-truth answers, such as mathematics and code generation. However, a significant gap remains for non-verifiable tasks, like creative writing and open-ended dialogue, where quality assessment is inherently subjective and lacks definitive references. Existing approaches for these domains often rely on scalar reward models trained with human preferences, which suffer from limited generalization and are prone to reward hacking, such as over-explanation and length bias. In this work, we propose a unified RLVR-based training paradigm that bridges the gap between non-verifiable tasks and verifiable rewards. We introduce a writing-principle-based pairwise Generative Reward Model (GenRM) and a novel Bootstrapped Relative Policy Optimization (BRPO) algorithm. The pairwise writing GenRM leverages self-principled critique to transform subjective assessments into reliable, verifiable rewards, while BRPO enables dynamic, reference-free pairwise comparison by leveraging a bootstrapped response as temporary reference from within group rollouts during RL training. Our approach empowers LLMs to develop robust writing capabilities without supervised fine-tuning, as demonstrated by Writing-Zero, which shows consistent improvement and strong resistance to reward hacking compared to scalar reward baselines. Furthermore, our method achieves competitive results on both in-house and open-source writing benchmarks. Our findings suggest the potential to unify rule-based, reference-based, and reference-free reward modeling under the RLVR framework, thus paving the way for a comprehensive and scalable RL training paradigm applicable across all language tasks.

Related papers

• R-Align: Enhancing Generative Reward Models through Rationale-Centric Meta-Judging [69.96389360650072]
  We show that reasoning fidelity is highly predictive of downstream RLHF outcomes, beyond standard label accuracy.<n>We propose Rationale-Centric Alignment, R-Align, which augments training with gold judgments and explicitly supervises rationale alignment.
  arXiv  Detail & Related papers  (2026-02-06T15:17:11Z)
• From Verifiable Dot to Reward Chain: Harnessing Verifiable Reference-based Rewards for Reinforcement Learning of Open-ended Generation [52.62655622099456]
  We propose reinforcement learning with verifiable reference-based rewards (RLVRR)<n>Instead of checking the final answer, RLVRR extracts an ordered linguistic signal from high-quality references (i.e., reward chain)<n>In this way, RLVRR decomposes rewards into two dimensions: content, which preserves deterministic core concepts, and style, which evaluates adherence to stylistic properties.
  arXiv  Detail & Related papers  (2026-01-26T14:39:58Z)
• Owen-Shapley Policy Optimization (OSPO): A Principled RL Algorithm for Generative Search LLMs [12.75200353208858]
  Owen-Shapley Policy Optimization (OSPO) is a framework that redistributes sequence-level advantages based on tokens' marginal contributions to outcomes.<n>Unlike value-model-based methods requiring additional computation, OSPO employs potential-based reward shaping via Shapley-Owen attributions to assign segment-level credit.<n> Experiments on Amazon ESCI and H&M Fashion datasets show consistent gains over baselines.
  arXiv  Detail & Related papers  (2026-01-13T10:17:46Z)
• Incorporating Self-Rewriting into Large Language Model Reasoning Reinforcement [54.63337314382886]
  We introduce self-rewriting framework, where a model rewrites its own reasoning texts, and subsequently learns from the rewritten reasoning to improve internal thought process quality.<n>For algorithm design, we propose a selective rewriting approach wherein only "simple" samples, defined by the model's consistent correctness, are rewritten.<n>Experiments on diverse tasks with different model sizes validate the effectiveness of self-rewriting.
  arXiv  Detail & Related papers  (2025-11-20T13:10:52Z)
• Auditable-choice reframing unlocks RL-based verification for open-ended tasks [23.12421867559344]
  Verifiable Multiple-Choice Reformulation (VMR) is a novel training strategy that restructures open-ended data into verifiable multiple-choice formats.<n>Across eight open-ended benchmarks, our VMR-based training delivers an average gain of 5.99 points over the baseline.
  arXiv  Detail & Related papers  (2025-11-04T10:45:52Z)
• OpenRubrics: Towards Scalable Synthetic Rubric Generation for Reward Modeling and LLM Alignment [38.1645520104553]
  We introduce OpenRubrics, a large-scale collection of (prompt,explicit) pairs for training rubric-generation and rubric-based reward models.<n>To elicit discriminative and comprehensive evaluation signals, we introduce Contrastive Generation (CRG), which derives both hard rules (implicit qualities) and principles (implicit qualities) by contrasting preferred and rejected responses.<n>Our results show that rubrics provide scalable alignment signals that narrow the gap between costly human evaluation and automated reward modeling.
  arXiv  Detail & Related papers  (2025-10-09T03:31:26Z)
• CAPO: Towards Enhancing LLM Reasoning through Verifiable Generative Credit Assignment [39.965170904699974]
  Reinforcement Learning with Verifiable Rewards (RLVR) has improved the reasoning abilities of Large Language Models (LLMs) by using rule-based binary feedback.<n>Current RLVR methods treat whole responses as single actions, assigning the same reward to every token.<n>This coarse-grained feedback hampers precise credit assignment, making it hard for models to identify which reasoning steps lead to success or failure.
  arXiv  Detail & Related papers  (2025-08-04T11:06:08Z)
• Rubrics as Rewards: Reinforcement Learning Beyond Verifiable Domains [8.143110220871614]
  We introduce RaR, a framework that uses structured, checklist-style rubrics as interpretable reward signals.<n>By treating rubrics as structured reward signals, we show that RaR enables smaller-scale judge models to better align with human preferences.
  arXiv  Detail & Related papers  (2025-07-23T17:57:55Z)
• RLPR: Extrapolating RLVR to General Domains without Verifiers [103.14103272635893]
  We propose RLPR, a simple verifier-free framework that extrapolates RLVR to broader general domains.<n>We find that addressing the high variance of this noisy probability reward is crucial to make it work.<n>RLPR consistently improves reasoning capabilities in both areas for Gemma, Llama, and Qwen based models.
  arXiv  Detail & Related papers  (2025-06-23T02:56:36Z)
• Intra-Trajectory Consistency for Reward Modeling [67.84522106537274]
  We develop an intra-trajectory consistency regularization to enforce that adjacent processes with higher next-token generation probability maintain more consistent rewards.<n>We show that the reward model trained with the proposed regularization induces better DPO-aligned policies and achieves better best-of-N (BON) inference-time verification results.
  arXiv  Detail & Related papers  (2025-06-10T12:59:14Z)
• QA-LIGN: Aligning LLMs through Constitutionally Decomposed QA [49.9801383018588]
  We introduce QA-LIGN, an automatic symbolic reward decomposition approach.<n>Instead of training a black-box reward model that outputs a monolithic score, QA-LIGN formulates principle-specific evaluation questions.<n>Experiments aligning an uncensored large language model with a set of constitutional principles demonstrate that QA-LIGN offers greater transparency and adaptability.
  arXiv  Detail & Related papers  (2025-06-09T18:24:57Z)
• RAG-Zeval: Towards Robust and Interpretable Evaluation on RAG Responses through End-to-End Rule-Guided Reasoning [64.46921169261852]
  RAG-Zeval is a novel end-to-end framework that formulates faithfulness and correctness evaluation as a rule-guided reasoning task.<n>Our approach trains evaluators with reinforcement learning, facilitating compact models to generate comprehensive and sound assessments.<n>Experiments demonstrate RAG-Zeval's superior performance, achieving the strongest correlation with human judgments.
  arXiv  Detail & Related papers  (2025-05-28T14:55:33Z)
• Learning to Reason without External Rewards [100.27210579418562]
  Training large language models (LLMs) for complex reasoning via Reinforcement Learning with Verifiable Rewards (RLVR) is effective but limited by reliance on costly, domain-specific supervision.<n>We explore Reinforcement Learning from Internal Feedback (RLIF), a framework that enables LLMs to learn from intrinsic signals without external rewards or labeled data.<n>We propose Intuitor, an RLIF method that uses a model's own confidence, termed self-certainty, as its sole reward signal.
  arXiv  Detail & Related papers  (2025-05-26T07:01:06Z)
• Crossing the Reward Bridge: Expanding RL with Verifiable Rewards Across Diverse Domains [92.36624674516553]
  Reinforcement learning with verifiable rewards (RLVR) has demonstrated significant success in enhancing mathematical reasoning and coding performance of large language models (LLMs)<n>We investigate the effectiveness and scalability of RLVR across diverse real-world domains including medicine, chemistry, psychology, economics, and education.<n>We utilize a generative scoring technique that yields soft, model-based reward signals to overcome limitations posed by binary verifications.
  arXiv  Detail & Related papers  (2025-03-31T08:22:49Z)
• Reinfier and Reintrainer: Verification and Interpretation-Driven Safe Deep Reinforcement Learning Frameworks [36.730973051834376]
  We propose a verification-driven interpretation-in-the-loop framework Reintrainer to develop trustworthy DRL models.<n>In each iteration, this framework measures the gap between the on-training model and predefined properties using formal verification.<n>Reinfier features breakpoints searching and verification-driven interpretation, associated with a concise constraint-encoding language DRLP.
  arXiv  Detail & Related papers  (2024-10-19T15:03:26Z)
• RewardBench: Evaluating Reward Models for Language Modeling [100.28366840977966]
  We present RewardBench, a benchmark dataset and code-base for evaluation of reward models. The dataset is a collection of prompt-chosen-rejected trios spanning chat, reasoning, and safety. On the RewardBench leaderboard, we evaluate reward models trained with a variety of methods.
  arXiv  Detail & Related papers  (2024-03-20T17:49:54Z)
• ALaRM: Align Language Models via Hierarchical Rewards Modeling [41.79125107279527]
  We introduce ALaRM, the first framework modeling hierarchical rewards in reinforcement learning from human feedback. The framework addresses the limitations of current alignment approaches, by integrating holistic rewards with aspect-specific rewards. We validate our approach through applications in long-form question answering and machine translation tasks.
  arXiv  Detail & Related papers  (2024-03-11T14:28:40Z)
• Beyond Sparse Rewards: Enhancing Reinforcement Learning with Language Model Critique in Text Generation [29.6763730290473]
  Reinforcement learning can align language models with non-differentiable reward signals, such as human preferences. This paper introduces a novel framework that utilizes the critique capability of Large Language Models to produce intermediate-step rewards.
  arXiv  Detail & Related papers  (2024-01-14T22:05:11Z)
• Text Generation with Efficient (Soft) Q-Learning [91.47743595382758]
  Reinforcement learning (RL) offers a more flexible solution by allowing users to plug in arbitrary task metrics as reward. We introduce a new RL formulation for text generation from the soft Q-learning perspective. We apply the approach to a wide range of tasks, including learning from noisy/negative examples, adversarial attacks, and prompt generation.
  arXiv  Detail & Related papers  (2021-06-14T18:48:40Z)

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.