CDRRM: Contrast-Driven Rubric Generation for Reliable and Interpretable Reward Modeling

• URL: http://arxiv.org/abs/2603.08035v1
• Date: Mon, 09 Mar 2026 07:15:23 GMT
• Title: CDRRM: Contrast-Driven Rubric Generation for Reliable and Interpretable Reward Modeling
• Abstract summary: CDRRM (Contrast-Driven Reward Model) is a framework for high-quality rubric generation and guided preference judgment.<n>This work offers a scalable, interpretable, and data-efficient path for reward modeling.
• Score: 61.75914342638658
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reward modeling is essential for aligning Large Language Models(LLMs) with human preferences, yet conventional reward models suffer from poor interpretability and heavy reliance on costly expert annotations. While recent rubric-based approaches enhance evaluation transparency, they lack systematic quality control, yielding noisy and redundant criteria, failing to mitigate persistent biases (e.g., verbosity, position) in LLM evaluators, and creating a scalability-reliability trade-off. To address these limitations, we propose CDRRM (Contrast-Driven Rubric Reward Model), a framework built on a novel Contrast-then-Synthesis paradigm for high-quality rubric generation and guided preference judgment. CDRRM first conducts multi-dimensional contrastive profiling on preference pairs to identify causal discriminative factors, then synthesizes these insights into compact, context-aware rubrics to guide preference judg- ments. Extensive experiments on three authoritative benchmarks (RewardBench, RMBench, RMB) demonstrate that CDRRM achieves state-of-the-art performance across diverse domains and effectively mitigates aforementioned evaluation biases. Notably, our approach delivers exceptional data efficiency: training the rubric generator on only 3k high-quality samples empowers a frozen pre-trained judge model to outperform fully fine-tuned baselines. This work offers a scalable, interpretable, and data-efficient path for reward modeling.

Related papers

• On Predicting the Post-training Potential of Pre-trained LLMs [60.07459271263409]
  We introduce a new task of predicting post-training potential - forecasting a base model's performance before post-training.<n>We propose RuDE, a unified framework that bypasses the generation gap of base models by leveraging response discrimination.<n>Experiments demonstrate a correlation greater than 90% with post-training performance.
  arXiv  Detail & Related papers  (2026-05-12T11:33:49Z)
• Optimal Transport for LLM Reward Modeling from Noisy Preference [21.518348751984433]
  We propose SelectiveRM, a framework grounded in optimal transport.<n>We first devise a Joint Consistency Discrepancy to align the distribution of model predictions with preference data.<n>We then incorporate a Mass Relaxation mechanism via partial transport.<n>This enables the autonomous exclusion of samples with noisy preference that contradict semantic consistency.
  arXiv  Detail & Related papers  (2026-05-07T11:26:04Z)
• MSA-Thinker: Discrimination-Calibration Reasoning with Hint-Guided Reinforcement Learning for Multimodal Sentiment Analysis [5.1150258716324055]
  Multimodal sentiment analysis aims to understand human emotions by integrating textual, auditory, and visual modalities.<n>Existing methods incorporating Chain-of-Thought (CoT) reasoning are hindered by high annotation costs.<n>We propose a novel training framework that integrates structured Discrimination-Calibration (DC) reasoning with Hint-based Reinforcement Learning.
  arXiv  Detail & Related papers  (2026-03-10T12:48:41Z)
• CAMEL: Confidence-Gated Reflection for Reward Modeling [26.908515245229747]
  We propose CAMEL, a confidence-gated reflection framework that performs a lightweight single-token preference decision first.<n>We train the model via reinforcement learning with counterfactual prefix augmentation, which exposes the model to diverse initial verdicts and encourages genuine revision.<n> Empirically, CAMEL achieves state-of-the-art performance on three widely used reward-model benchmarks with 82.9% average accuracy.
  arXiv  Detail & Related papers  (2026-02-24T08:20:08Z)
• Learning Ordinal Probabilistic Reward from Preferences [25.069054134899744]
  We introduce a novel reward modeling paradigm: Probabilistic Reward Model (PRM)<n>Instead of modeling reward as a deterministic scalar, our approach treats it as a random variable, learning a full probability distribution for the quality of each response.<n>Building on OPRM, we propose a data-efficient training strategy called Region Flooding Tuning (RgFT)
  arXiv  Detail & Related papers  (2026-02-13T06:43:02Z)
• Mitigating Reward Hacking in RLHF via Bayesian Non-negative Reward Modeling [49.41422138354821]
  We propose a principled reward modeling framework that integrates non-negative factor analysis into the Bradley-Terry preference model.<n>BNRM represents rewards through a sparse, non-negative latent factor generative process.<n>We show that BNRM substantially mitigates reward over-optimization, improves robustness under distribution shifts, and yields more interpretable reward decompositions than strong baselines.
  arXiv  Detail & Related papers  (2026-02-11T08:14:11Z)
• SoliReward: Mitigating Susceptibility to Reward Hacking and Annotation Noise in Video Generation Reward Models [53.19726629537694]
  Post-training alignment of video generation models with human preferences is a critical goal.<n>Current data collection paradigms, reliant on in-prompt pairwise annotations, suffer from labeling noise.<n>We propose SoliReward, a systematic framework for video RM training.
  arXiv  Detail & Related papers  (2025-12-17T14:28:23Z)
• 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)
• T2I-Eval-R1: Reinforcement Learning-Driven Reasoning for Interpretable Text-to-Image Evaluation [60.620408007636016]
  We propose T2I-Eval-R1, a novel reinforcement learning framework that trains open-source MLLMs using only coarse-grained quality scores.<n>Our approach integrates Group Relative Policy Optimization into the instruction-tuning process, enabling models to generate both scalar scores and interpretable reasoning chains.
  arXiv  Detail & Related papers  (2025-05-23T13:44:59Z)
• Modeling the Q-Diversity in a Min-max Play Game for Robust Optimization [61.39201891894024]
  Group distributionally robust optimization (group DRO) can minimize the worst-case loss over pre-defined groups. We reformulate the group DRO framework by proposing Q-Diversity. Characterized by an interactive training mode, Q-Diversity relaxes the group identification from annotation into direct parameterization.
  arXiv  Detail & Related papers  (2023-05-20T07:02:27Z)
• Distributionally Robust Models with Parametric Likelihood Ratios [123.05074253513935]
  Three simple ideas allow us to train models with DRO using a broader class of parametric likelihood ratios. We find that models trained with the resulting parametric adversaries are consistently more robust to subpopulation shifts when compared to other DRO approaches.
  arXiv  Detail & Related papers  (2022-04-13T12:43:12Z)

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.