R3: Robust Rubric-Agnostic Reward Models

• URL: http://arxiv.org/abs/2505.13388v3
• Date: Fri, 19 Sep 2025 22:07:47 GMT
• Title: R3: Robust Rubric-Agnostic Reward Models
• Authors: David Anugraha, Zilu Tang, Lester James V. Miranda, Hanyang Zhao, Mohammad Rifqi Farhansyah, Garry Kuwanto, Derry Wijaya, Genta Indra Winata,
• Abstract summary: $shortmethodname$ is a novel reward modeling framework that is rubric-agnostic, generalizable across evaluation dimensions, and provides interpretable, reasoned score assignments.<n>Our models, data, and code are available as open source at https://github.com/rubricreward/r3.
• Score: 17.958272227199746
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reward models are essential for aligning language model outputs with human preferences, yet existing approaches often lack both controllability and interpretability. These models are typically optimized for narrow objectives, limiting their generalizability to broader downstream tasks. Moreover, their scalar outputs are difficult to interpret without contextual reasoning. To address these limitations, we introduce $\shortmethodname$, a novel reward modeling framework that is rubric-agnostic, generalizable across evaluation dimensions, and provides interpretable, reasoned score assignments. $\shortmethodname$ enables more transparent and flexible evaluation of language models, supporting robust alignment with diverse human values and use cases. Our models, data, and code are available as open source at https://github.com/rubricreward/r3.

Related papers

• Auto-Rubric: Learning to Extract Generalizable Criteria for Reward Modeling [37.237020102873]
  Reward models are essential for aligning Large Language Models with human values, yet their development is hampered by costly preference datasets and poor interpretability.<n>We build a training-free framework that infers high-quality, query-specific rubrics using a validation-guided textbfPropose-Evaluate-Revise pipeline.<n>Using just 70 preference pairs (1.5% of the source data), our method also empowers smaller models like Qwen3-8B to outperform specialized, fully-trained counterparts.
  arXiv  Detail & Related papers  (2025-10-20T09:01:37Z)
• Relic: Enhancing Reward Model Generalization for Low-Resource Indic Languages with Few-Shot Examples [58.55904048776596]
  Most open-source multilingual reward models are primarily trained on preference datasets in high-resource languages.<n>We propose RELIC, a novel in-context learning framework for reward modeling in low-resource Indic languages.
  arXiv  Detail & Related papers  (2025-06-19T17:56:16Z)
• Pruning General Large Language Models into Customized Expert Models [78.96891010334852]
  Large language models (LLMs) have revolutionized natural language processing, yet their substantial model sizes often require substantial computational resources.<n>We propose a method to prune a large general model into a smaller lightweight expert model.<n>By identifying and pruning irrelevant neurons of each dimension, $textttCus-Prun$ creates expert models without any post-training.
  arXiv  Detail & Related papers  (2025-06-03T07:47:30Z)
• Language Models can Self-Improve at State-Value Estimation for Better Search [23.61729554517216]
  We introduce Self-Taught Lookahead (STL), a reward-free framework that improves language model-based value functions by reasoning explicitly about state transitions.<n>We find that STL enables small open-source models to guide efficient search, reducing inference costs by integrating explicit reasoning with value learning.
  arXiv  Detail & Related papers  (2025-03-04T18:58:11Z)
• Predicting the Performance of Black-box LLMs through Self-Queries [60.87193950962585]
  Large language models (LLMs) are increasingly relied on in AI systems, predicting when they make mistakes is crucial.<n>In this paper, we extract features of LLMs in a black-box manner by using follow-up prompts and taking the probabilities of different responses as representations.<n>We demonstrate that training a linear model on these low-dimensional representations produces reliable predictors of model performance at the instance level.
  arXiv  Detail & Related papers  (2025-01-02T22:26:54Z)
• SPaR: Self-Play with Tree-Search Refinement to Improve Instruction-Following in Large Language Models [88.29990536278167]
  We introduce SPaR, a self-play framework integrating tree-search self-refinement to yield valid and comparable preference pairs.<n>Our experiments show that a LLaMA3-8B model, trained over three iterations guided by SPaR, surpasses GPT-4-Turbo on the IFEval benchmark without losing general capabilities.
  arXiv  Detail & Related papers  (2024-12-16T09:47:43Z)
• Interpret the Internal States of Recommendation Model with Sparse Autoencoder [28.234859617081295]
  RecSAE is an automated and generalizable probing framework that interprets Recommenders with Sparse AutoEncoder.<n>It extracts interpretable latents from the internal states of recommendation models and links them to semantic concepts for interpretation.<n> RecSAE does not alter original models during interpretation and also enables targeted de-biasing to models based on interpreted results.
  arXiv  Detail & Related papers  (2024-11-09T08:22:31Z)
• 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)
• AXOLOTL: Fairness through Assisted Self-Debiasing of Large Language Model Outputs [20.772266479533776]
  AXOLOTL is a novel post-processing framework that operates agnostically across tasks and models. It identifies biases, proposes resolutions, and guides the model to self-debias its outputs. This approach minimizes computational costs and preserves model performance.
  arXiv  Detail & Related papers  (2024-03-01T00:02:37Z)
• Non-Vacuous Generalization Bounds for Large Language Models [78.42762571499061]
  We provide the first non-vacuous generalization bounds for pretrained large language models. We show that larger models have better generalization bounds and are more compressible than smaller models.
  arXiv  Detail & Related papers  (2023-12-28T17:58:42Z)
• RDR: the Recap, Deliberate, and Respond Method for Enhanced Language Understanding [6.738409533239947]
  The Recap, Deliberate, and Respond (RDR) paradigm addresses this issue by incorporating three distinct objectives within the neural network pipeline. By cascading these three models, we mitigate the potential for gaming the benchmark and establish a robust method for capturing the underlying semantic patterns. Our results demonstrate improved performance compared to competitive baselines, with an enhancement of up to 2% on standard metrics.
  arXiv  Detail & Related papers  (2023-12-15T16:41:48Z)
• RecExplainer: Aligning Large Language Models for Explaining Recommendation Models [50.74181089742969]
  Large language models (LLMs) have demonstrated remarkable intelligence in understanding, reasoning, and instruction following. This paper presents the initial exploration of using LLMs as surrogate models to explain black-box recommender models. To facilitate an effective alignment, we introduce three methods: behavior alignment, intention alignment, and hybrid alignment.
  arXiv  Detail & Related papers  (2023-11-18T03:05:43Z)
• Assessing and Enhancing the Robustness of Large Language Models with Task Structure Variations for Logical Reasoning [25.496627355906966]
  We develop three new logical reasoning datasets named "ReClor-plus", "LogiQA-plus" and "LogiQAv2-plus"<n>Experiments show that these simple augmentations greatly hinder the models' performance.<n>Applying logic-driven data augmentation for fine-tuning and prompting can enhance generalisation in both discriminative and generative models.
  arXiv  Detail & Related papers  (2023-10-13T22:29:15Z)
• Interpretability at Scale: Identifying Causal Mechanisms in Alpaca [62.65877150123775]
  We use Boundless DAS to efficiently search for interpretable causal structure in large language models while they follow instructions. Our findings mark a first step toward faithfully understanding the inner-workings of our ever-growing and most widely deployed language models.
  arXiv  Detail & Related papers  (2023-05-15T17:15:40Z)
• Benchmarking the Robustness of LiDAR Semantic Segmentation Models [78.6597530416523]
  In this paper, we aim to comprehensively analyze the robustness of LiDAR semantic segmentation models under various corruptions. We propose a new benchmark called SemanticKITTI-C, which features 16 out-of-domain LiDAR corruptions in three groups, namely adverse weather, measurement noise and cross-device discrepancy. We design a robust LiDAR segmentation model (RLSeg) which greatly boosts the robustness with simple but effective modifications.
  arXiv  Detail & Related papers  (2023-01-03T06:47:31Z)
• Investigating Ensemble Methods for Model Robustness Improvement of Text Classifiers [66.36045164286854]
  We analyze a set of existing bias features and demonstrate there is no single model that works best for all the cases. By choosing an appropriate bias model, we can obtain a better robustness result than baselines with a more sophisticated model design.
  arXiv  Detail & Related papers  (2022-10-28T17:52:10Z)
• An Understanding-Oriented Robust Machine Reading Comprehension Model [12.870425062204035]
  We propose an understanding-oriented machine reading comprehension model to address three kinds of robustness issues. Specifically, we first use a natural language inference module to help the model understand the accurate semantic meanings of input questions. Third, we propose a multilanguage learning mechanism to address the issue of generalization.
  arXiv  Detail & Related papers  (2022-07-01T03:32:02Z)
• Combining Discrete Choice Models and Neural Networks through Embeddings: Formulation, Interpretability and Performance [10.57079240576682]
  This study proposes a novel approach that combines theory and data-driven choice models using Artificial Neural Networks (ANNs) In particular, we use continuous vector representations, called embeddings, for encoding categorical or discrete explanatory variables. Our models deliver state-of-the-art predictive performance, outperforming existing ANN-based models while drastically reducing the number of required network parameters.
  arXiv  Detail & Related papers  (2021-09-24T15:55:31Z)

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.