{"total":132,"rows":[{"id":"2602.01511","title":"Alternating Reinforcement Learning for Rubric-Based Reward Modeling in Non-Verifiable LLM Post-Training","created":"2026-02-02","cited_by_count":54,"area":"alignment"},{"id":"2601.08430","title":"RubricHub: A Comprehensive and Highly Discriminative Rubric Dataset via Automated Coarse-to-Fine Generation","created":"2026-01-13","cited_by_count":52,"area":null},{"id":"2604.13602","title":"Reward Hacking in the Era of Large Models: Mechanisms, Emergent Misalignment, Challenges","created":"2026-04-15","cited_by_count":33,"area":"alignment"},{"id":"2510.17314","title":"Auto-Rubric: Learning From Implicit Weights to Explicit Rubrics for Reward Modeling","created":"2025-10-20","cited_by_count":30,"area":"alignment"},{"id":"2603.01562","title":"RubricBench: Aligning Model-Generated Rubrics with Human Standards","created":"2026-03-02","cited_by_count":28,"area":"alignment"},{"id":"2602.14069","title":"Open Rubric System: Scaling Reinforcement Learning with Pairwise Adaptive Rubric","created":"2026-02-15","cited_by_count":27,"area":"alignment"},{"id":"2602.03619","title":"Learning Query-Specific Rubrics from Human Preferences for DeepResearch Report Generation","created":"2026-02-03","cited_by_count":18,"area":"alignment"},{"id":"2510.23038","title":"Incentivizing Agentic Reasoning in LLM Judges via Tool-Integrated Reinforcement Learning","created":"2025-10-27","cited_by_count":17,"area":"lr"},{"id":"2601.06487","title":"ArenaRL: Scaling RL for Open-Ended Agents via Tournament-based Relative Ranking","created":"2026-01-10","cited_by_count":17,"area":"agents"},{"id":"2606.12507","title":"Rubric-Guided Self-Distillation: Post-Training Without Rubric Verifiers","created":"2026-06-10","cited_by_count":15,"area":"distill"},{"id":"2602.12268","title":"CM2: Reinforcement Learning with Checklist Rewards for Multi-Turn and Multi-Step Agentic Tool Use","created":"2026-02-12","cited_by_count":14,"area":"agents"},{"id":"2606.23038","title":"EvoRubrics: Dynamic Rubrics as Rewards via Adversarial Co-Evolution for LLM Reinforcement Learning","created":"2026-06-22","cited_by_count":14,"area":"alignment"},{"id":"2605.07396","title":"Rubric-based On-policy Distillation","created":"2026-05-08","cited_by_count":13,"area":"alignment"},{"id":"2510.14738","title":"AutoRubric: Rubric-Based Generative Rewards for Faithful Multimodal Reasoning","created":"2025-10-16","cited_by_count":11,"area":"lr"},{"id":"2603.20882","title":"RubricRAG: Towards Interpretable and Reliable LLM Evaluation via Domain Knowledge Retrieval for Rubric Generation","created":"2026-03-21","cited_by_count":11,"area":null},{"id":"2605.20164","title":"Not Every Rubric Teaches Equally: Policy-Aware Rubric Rewards for RLVR","created":"2026-05-19","cited_by_count":11,"area":null},{"id":"2602.02301","title":"Advancing General-Purpose Reasoning Models with Modular Gradient Surgery","created":"2026-02-02","cited_by_count":9,"area":"lr"},{"id":"2602.09953","title":"ATTNPO: Attention-Guided Process Supervision for Efficient Reasoning","created":"2026-02-10","cited_by_count":9,"area":"lr"},{"id":"2608.11669","title":"Rubric Dropout: A Simple Way to Mitigate Reward Hacking in Rubric-as-Reward RL","created":"2026-08-12","cited_by_count":9,"area":"alignment"},{"id":"2603.23522","title":"Qworld: Question-Specific Evaluation Criteria for LLMs","created":"2026-03-06","cited_by_count":8,"area":null},{"id":"2602.00846","title":"Omni-RRM: Advancing Omni Reward Modeling via Automatic Rubric-Grounded Preference Synthesis","created":"2026-01-31","cited_by_count":7,"area":"mllm"},{"id":"2603.02637","title":"StitchCUDA: An Automated Multi-Agents End-to-End GPU Programing Framework with Rubric-based Agentic Reinforcement Learning","created":"2026-03-03","cited_by_count":7,"area":"agents"},{"id":"2605.31433","title":"SCOPE: Self-Play via Co-Evolving Policies for Open-Ended Tasks","created":"2026-05-29","cited_by_count":7,"area":"synth"},{"id":"2602.20751","title":"SibylSense: Adaptive Rubric Learning via Memory Tuning and Adversarial Probing","created":"2026-02-24","cited_by_count":6,"area":null},{"id":"2607.12835","title":"Can LLMs Write Reliable Rubrics? A Meta-Evaluation for Experiment Reproduction","created":"2026-07-14","cited_by_count":6,"area":"agents"},{"id":"2601.02986","title":"P-Check: Advancing Personalized Reward Model via Learning to Generate Dynamic Checklist","created":"2026-01-06","cited_by_count":5,"area":"alignment"},{"id":"2603.07019","title":"AutoChecklist: Composable Pipelines for Checklist Generation and Scoring with LLM-as-a-Judge","created":"2026-03-07","cited_by_count":5,"area":null},{"id":"2604.13029","title":"Visual Preference Optimization with Rubric Rewards","created":"2026-04-14","cited_by_count":5,"area":"alignment"},{"id":"2605.25240","title":"JudgmentBench: Comparing Rubric and Preference Evaluation for Quality Assessment","created":"2026-05-24","cited_by_count":5,"area":null},{"id":"2605.29156","title":"RUBRIC-ARROW: Alternating Pointwise Rubric Reward Modeling for LLM Post-training in Non-verifiable Domains","created":"2026-05-27","cited_by_count":5,"area":"alignment"},{"id":"2511.01014","title":"IF-CRITIC: Towards a Fine-Grained LLM Critic for Instruction-Following Evaluation","created":"2025-11-02","cited_by_count":4,"area":null},{"id":"2602.13576","title":"Rubrics as an Attack Surface: Stealthy Preference Drift in LLM Judges","created":"2026-02-14","cited_by_count":4,"area":"alignment"},{"id":"2603.11027","title":"Beyond the Illusion of Consensus: From Surface Heuristics to Knowledge-Grounded Evaluation in LLM-as-a-Judge","created":"2026-03-11","cited_by_count":4,"area":null},{"id":"2604.07837","title":"SPARD: Self-Paced Curriculum for RL Alignment via Integrating Reward Dynamics and Data Utility","created":"2026-04-09","cited_by_count":4,"area":null},{"id":"2605.07461","title":"Think-with-Rubrics: From External Evaluator to Internal Reasoning Guidance","created":"2026-05-08","cited_by_count":4,"area":"lr"},{"id":"2605.26579","title":"Focal Reward: Balanced Reinforcement Learning under Rubric-Based Rewards","created":"2026-05-26","cited_by_count":4,"area":"alignment"},{"id":"2605.30244","title":"Reinforcement Learning with Robust Rubric Rewards","created":"2026-05-28","cited_by_count":4,"area":"lr"},{"id":"2606.08625","title":"From Holistic Evaluation to Structured Criteria: Rubrics Across the Evolving LLM Landscape","created":"2026-06-07","cited_by_count":4,"area":null},{"id":"2606.21262","title":"ARCO: Adaptive Rubrics with Co-Evolution for Multi-Step LLM-Based Agents","created":"2026-06-19","cited_by_count":4,"area":"agents"},{"id":"2608.02948","title":"Rubrics as Privileged Information for Open-Ended Generation","created":"2026-08-03","cited_by_count":4,"area":"distill"},{"id":"2608.23566","title":"Best Practice Critic Optimization","created":"2026-08-24","cited_by_count":4,"area":"lr"},{"id":"2512.06196","title":"ARCANE: A Multi-Agent Framework for Interpretable and Configurable Alignment","created":"2025-12-05","cited_by_count":3,"area":"agents"},{"id":"2602.22751","title":"Know What You Know: Metacognitive Entropy Calibration for Verifiable RL Reasoning","created":"2026-02-26","cited_by_count":3,"area":"lr"},{"id":"2604.01375","title":"The Hitchhikers Guide to Rubric Quality Understanding and Enrichment","created":"2026-04-01","cited_by_count":3,"area":null},{"id":"2604.13618","title":"C2: Scalable Rubric-Augmented Reward Modeling from Binary Preferences","created":"2026-04-15","cited_by_count":3,"area":"alignment"},{"id":"2604.27093","title":"Useless but Safe? Benchmarking Utility Recovery with User Intent Clarification in Multi-Turn Conversations","created":"2026-04-29","cited_by_count":3,"area":"alignment"},{"id":"2605.09269","title":"DeltaRubric: Generative Multimodal Reward Modeling via Joint Planning and Verification","created":"2026-05-10","cited_by_count":3,"area":"mllm"},{"id":"2605.18592","title":"AMARIS: A Memory-Augmented Rubric Improvement System for Rubric-Based Reinforcement Learning","created":"2026-05-18","cited_by_count":3,"area":"alignment"},{"id":"2606.03980","title":"Skill-RM: Unifying Heterogeneous Evaluation Criteria via Agent Skill","created":"2026-06-02","cited_by_count":3,"area":"agents"},{"id":"2607.01830","title":"Many Voices, One Reward: Multi-Role Rubric Generation for LLM Judging and Reward Modeling","created":"2026-07-02","cited_by_count":3,"area":"alignment"},{"id":"2607.05150","title":"Claim-Level Rubric Rewards for Video Caption Reinforcement Learning","created":"2026-07-06","cited_by_count":3,"area":"alignment"},{"id":"2608.02831","title":"Reinforcement Learning with Evolving Rubrics as Rewards for Audio Reasoning","created":"2026-08-03","cited_by_count":3,"area":null},{"id":"2608.27505","title":"A Survey on Rubric-Guided Reinforcement Learning for Language Models","created":"2026-08-27","cited_by_count":3,"area":"alignment"},{"id":"2601.11374","title":"Reward Modeling for Scientific Writing Evaluation","created":"2026-01-16","cited_by_count":2,"area":"lr"},{"id":"2602.11661","title":"Quark Medical Alignment: A Holistic Multi-Dimensional Alignment and Collaborative Optimization Paradigm","created":"2026-02-12","cited_by_count":2,"area":"alignment"},{"id":"2603.27977","title":"SARL: Label-Free Reinforcement Learning by Rewarding Reasoning Topology","created":"2026-03-30","cited_by_count":2,"area":"lr"},{"id":"2605.13486","title":"R^2-Mem: Reflective Experience for Memory Search","created":"2026-05-13","cited_by_count":2,"area":null},{"id":"2605.29310","title":"Rubric-Guided Process Reward for Stepwise Model Routing","created":"2026-05-28","cited_by_count":2,"area":"lr"},{"id":"2605.31545","title":"Preference-Aware Rubric Learning for Personalized Evaluation","created":"2026-05-29","cited_by_count":2,"area":"alignment"},{"id":"2606.01091","title":"Deep Research as Rubric for Reinforcement Learning","created":"2026-05-31","cited_by_count":2,"area":"agents"},{"id":"2606.04051","title":"RUBAS: Rubric-Based Reinforcement Learning for Agent Safety","created":"2026-06-02","cited_by_count":2,"area":"agents"},{"id":"2606.12360","title":"Anatomy of Post-Training: Using Interpretability to Characterize Data and Shape the Learning Signal","created":"2026-06-10","cited_by_count":2,"area":null},{"id":"2607.15092","title":"Rubrics on Trial: Evolving Rubrics from a Single Query via Synthetic Pairwise Evidence","created":"2026-07-16","cited_by_count":2,"area":null},{"id":"2607.25675","title":"DecoEvo: Score-Decoupled Co-Evolution of Solver and Rubric-Generator Skills in Text Space","created":"2026-07-28","cited_by_count":2,"area":null},{"id":"2608.00005","title":"RubricReviewer: From Direct Critique to Objective and Comprehensive Rubric-Driven Peer Review","created":"2026-06-04","cited_by_count":2,"area":null},{"id":"2608.05141","title":"OctoLong: Mid-Training On Cross-Repository Code Contexts Enhances Long-Context Modeling","created":"2026-08-05","cited_by_count":2,"area":"agents"},{"id":"2608.24138","title":"Rubrics as Visual-Repair Context for Self-Evolving UI-to-Code Generation","created":"2026-08-25","cited_by_count":2,"area":"mllm"},{"id":"2608.26956","title":"RubricRM: Generative Reward Modeling via Dynamic Rubrics for Image Generation and Editing","created":"2026-08-27","cited_by_count":2,"area":"alignment"},{"id":"2608.30005","title":"Small Language Models as Judges for Rubric-Based Reinforcement Learning","created":"2026-08-30","cited_by_count":2,"area":null},{"id":"2609.11117","title":"Overview of the NLPCC 2026 Shared Task 11: Agent-Based Experiment Reproduction from Scientific Papers","created":"2026-09-10","cited_by_count":2,"area":"agents"},{"id":"2609.36893","title":"Momentum-Coupled Rubric Adaptation for Detailed Image Captioning","created":"2026-09-29","cited_by_count":2,"area":null},{"id":"2601.22735","title":"MM-THEBench: Do Reasoning MLLMs Think Reasonably?","created":"2026-01-30","cited_by_count":1,"area":"lr"},{"id":"2603.01571","title":"Beyond Length Scaling: Synergizing Breadth and Depth for Generative Reward Models","created":"2026-03-02","cited_by_count":1,"area":"lr"},{"id":"2604.28181","title":"Synthetic Computers at Scale for Long-Horizon Productivity Simulation","created":"2026-04-30","cited_by_count":1,"area":null},{"id":"2605.00754","title":"Themis: Training Robust Multilingual Code Reward Models for Flexible Multi-Criteria Scoring","created":"2026-05-01","cited_by_count":1,"area":"lr"},{"id":"2605.13641","title":"Multi-Objective and Mixed-Reward Reinforcement Learning via Reward-Decorrelated Policy Optimization","created":"2026-05-13","cited_by_count":1,"area":"deeprl"},{"id":"2605.20873","title":"PlanningBench: Generating Scalable and Verifiable Planning Data for Evaluating and Training Large Language Models","created":"2026-05-20","cited_by_count":1,"area":null},{"id":"2605.26442","title":"Alignment Tuning for Large Language Models: A Data-Centric Lens on Alignment Data Pipelines","created":"2026-04-07","cited_by_count":1,"area":null},{"id":"2605.29275","title":"Prompt-Level Reward Specifications for Open-Ended Post-Training","created":"2026-05-28","cited_by_count":1,"area":"alignment"},{"id":"2606.08077","title":"Support Vector Rubrics: Closing the Gap Between Self-Generated and Human Rubrics","created":"2026-06-06","cited_by_count":1,"area":"synth"},{"id":"2607.02175","title":"A rubric-based controlled comparison of frontier language models on expert-authored clinical reasoning tasks","created":"2026-07-02","cited_by_count":1,"area":"lr"},{"id":"2608.04077","title":"FinProBench: Evaluating Financial AI Agents with Role-Grounded Rubrics Derived from Professional Deliverables","created":"2026-08-04","cited_by_count":1,"area":"agents"},{"id":"2608.08612","title":"REVEAL: A Rubric-Guided Agent for Explicit Evidence Sufficiency Verificationin Long-Video Question Answering","created":"2026-08-09","cited_by_count":1,"area":"rag"},{"id":"2608.10996","title":"ConRub-Med: Reinforcement Learning with Consensus Rubrics for Open-Ended Medical Question Answering","created":"2026-08-11","cited_by_count":1,"area":"deeprl"},{"id":"2608.12097","title":"Graph-Structured Rubrics: Compiling Rubrics into Typed Evaluation Graphs for LLM Judges","created":"2026-08-12","cited_by_count":1,"area":null},{"id":"2608.27487","title":"Grounded Checklist Partial Credit for Agent Skill Trajectories","created":"2026-08-26","cited_by_count":1,"area":"agents"},{"id":"2608.30397","title":"Co-Evolving Actor-Conditioned Critics for Non-Verifiable Generation","created":"2026-08-31","cited_by_count":1,"area":null},{"id":"2609.00892","title":"CARE: Contrastive Anchor-based Rubric Evolution for Large Language Model Post-Training","created":"2026-09-01","cited_by_count":1,"area":null},{"id":"2609.01572","title":"From Production Traffic to Post-Training: Building a Self-Hosted LLM That Covers the Corporate Request Mix","created":"2026-09-01","cited_by_count":1,"area":null},{"id":"2609.12459","title":"EvoRS: On-Policy Self-Evolution of Reward Systems for Open-Ended Reinforcement Learning","created":"2026-09-11","cited_by_count":1,"area":"lr"},{"id":"2609.16816","title":"ImpossibleRubrics: Stress-Testing Generated Rubrics as Reward Signals","created":"2026-09-15","cited_by_count":1,"area":null},{"id":"2609.22947","title":"RewardVerse: Rubric-Guided Policy Optimization for Video Reward Modeling","created":"2026-09-19","cited_by_count":1,"area":"alignment"},{"id":"2610.05308","title":"RubricArmor: Adversarial Evolution Improves LLM-Based Rubric Generation","created":"2026-10-04","cited_by_count":1,"area":"alignment"},{"id":"2511.20194","title":"In-Context Compositional Learning via Sparse Coding Transformer","created":"2025-11-25","cited_by_count":0,"area":null},{"id":"2601.18253","title":"BoRP: Bootstrapped Regression Probing for Scalable and Human-Aligned LLM Evaluation","created":"2026-01-26","cited_by_count":0,"area":"synth"},{"id":"2602.15338","title":"Discovering Implicit Large Language Model Alignment Objectives","created":"2026-02-17","cited_by_count":0,"area":"alignment"},{"id":"2604.23809","title":"LegalDrill: Diagnosis-Driven Synthesis for Legal Reasoning in Small Language Models","created":"2026-04-26","cited_by_count":0,"area":"lr"},{"id":"2605.19665","title":"CriterAlign: Criterion-Centric Rationale Alignment for Code Preference Judging","created":"2026-05-19","cited_by_count":0,"area":"alignment"},{"id":"2605.23382","title":"From Correctness to Preference: A Framework for Personalized Agentic Reinforcement Learning","created":"2026-05-22","cited_by_count":0,"area":"agents"},{"id":"2605.23384","title":"Metacognition as Reward: Reinforcing LLM Reasoning via Knowledge and Regulation Signals","created":"2026-05-22","cited_by_count":0,"area":"lr"}],"next_cursor":"100"}