REMOR: Automated Peer Review Generation with LLM Reasoning and Multi-Objective Reinforcement Learning

• URL: http://arxiv.org/abs/2505.11718v2
• Date: Fri, 27 Jun 2025 02:48:27 GMT
• Title: REMOR: Automated Peer Review Generation with LLM Reasoning and Multi-Objective Reinforcement Learning
• Abstract summary: We evaluate how well a reasoning LLM trained with multi-objective reinforcement learning (REMOR) can overcome limitations.<n>REMOR achieves more than twice the average rewards of human reviews, non-reasoning state-of-the-art agentic multi-modal AI review systems.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: AI-based peer review systems tend to produce shallow and overpraising suggestions compared to human feedback. Here, we evaluate how well a reasoning LLM trained with multi-objective reinforcement learning (REMOR) can overcome these limitations. We start by designing a multi-aspect reward function that aligns with human evaluation of reviews. The aspects are related to the review itself (e.g., criticisms, novelty) and the relationship between the review and the manuscript (i.e., relevance). First, we perform supervised fine-tuning of DeepSeek-R1-Distill-Qwen-7B using LoRA on PeerRT, a new dataset of high-quality top AI conference reviews enriched with reasoning traces. We then apply Group Relative Policy Optimization (GRPO) to train two models: REMOR-H (with the human-aligned reward) and REMOR-U (with a uniform reward). Interestingly, the human-aligned reward penalizes aspects typically associated with strong reviews, leading REMOR-U to produce qualitatively more substantive feedback. Our results show that REMOR-U and REMOR-H achieve more than twice the average rewards of human reviews, non-reasoning state-of-the-art agentic multi-modal AI review systems, and general commercial LLM baselines. We found that while the best AI and human reviews are comparable in quality, REMOR avoids the long tail of low-quality human reviews. We discuss how reasoning is key to achieving these improvements and release the Human-aligned Peer Review Reward (HPRR) function, the Peer Review Reasoning-enriched Traces (PeerRT) dataset, and the REMOR models, which we believe can help spur progress in the area.

Related papers

• ActReview: Rebuttal-Guided Training Data and Rubric Rewards for Actionable Peer Review Generation [54.82704239643649]
  We introduce ActReview, a rebuttal-guided post-training framework that connects paper-specific diagnoses to concrete, grounded revision plans.<n>Our central insight is that author rebuttals reveal plausible actions for addressing reviewer concerns and can therefore provide latent supervision for revision-oriented feedback.<n>We also introduce ActReview-Bench, a human-curated benchmark of 1,000 instances for evaluating diagnostic quality and revision usefulness.
  arXiv  Detail & Related papers  (2026-09-08T17:30:16Z)
• Consistently Good vs. Occasionally Great: A Rubric for Open-Ended Feedback Quality from Humans and Machines [3.4020399898813145]
  We focus on feedback for open-ended short answer questions in introductory programming.<n>We develop a five-criteria rubric grounded in educational literature for evaluating feedback quality.
  arXiv  Detail & Related papers  (2026-08-22T08:48:53Z)
• Beyond Score Prediction: LLM-Based Essay Scoring and Feedback Generation via Reinforcement Learning with Rubric Rewards [6.002323709229882]
  Large language models (LLMs) have been widely applied to automated essay scoring (AES) and automated feedback generation (AFG)<n>We propose RLAES, a unified LLM framework that jointly optimize essay scoring and feedback generation through RL.<n>To make feedback quality measurable, interpretable, and usable for training, we introduce RL-based Feedback Evaluation (RFE)
  arXiv  Detail & Related papers  (2026-07-21T15:49:02Z)
• Stop Automating Peer Review Without Rigorous Evaluation [51.53099943385505]
  This position paper argues that today's AI systems should not be used to produce paper reviews.<n>We identify two critical issues: 1) AI reviewers exhibit a hivemind effect of excessive agreement within and across papers that reduces perspective diversity.<n>We argue that addressing the peer review crisis requires a science of peer review automation.
  arXiv  Detail & Related papers  (2026-05-04T22:41:04Z)
• Expanding the Capabilities of Reinforcement Learning via Text Feedback [49.561885700139676]
  We formalize a multi-turn RL setup, RL from Text Feedback (RLTF), where text feedback is available during training but not at inference.<n>To do this, we propose two methods: Self Distillation (RLTF-SD), which trains the single-turn policy to match its own feedback-conditioned second-turn generations; and Feedback Modeling (RLTF-FM), which predicts the feedback as an auxiliary objective.<n>Our results show that both methods consistently outperform strong baselines across benchmarks.
  arXiv  Detail & Related papers  (2026-02-02T18:56:56Z)
• Reward Modeling from Natural Language Human Feedback [77.75758630455357]
  Reinforcement Learning with Verifiable reward (RLVR) on preference data has become the mainstream approach for training Generative Reward Models (GRMs)<n>In this paper, we demonstrate that such binary classification tasks make GRMs susceptible to guessing correct outcomes without sound critiques.<n>We propose Reward Modeling from Natural Language Human Feedback (RM-NLHF), which leverages natural language feedback to obtain process reward signals.
  arXiv  Detail & Related papers  (2026-01-12T09:23:43Z)
• Off-Policy Corrected Reward Modeling for Reinforcement Learning from Human Feedback [52.1410307583181]
  We useReinforcement Learning from Human Feedback to train language models (LMs) to follow complex human preferences.<n>As training progresses, the responses generated by the LM no longer resemble the responses seen by the reward model (RM)<n>We propose Off-Policy Corrected Reward Modeling to correct the RM using importance weighting, without requiring new labels or samples.
  arXiv  Detail & Related papers  (2025-07-21T11:19:04Z)
• 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)
• Pairwise or Pointwise? Evaluating Feedback Protocols for Bias in LLM-Based Evaluation [57.380464382910375]
  We show that the choice of feedback protocol can significantly affect evaluation reliability and induce systematic biases.<n>In particular, we show that pairwise evaluation protocols are more vulnerable to distracted evaluation.
  arXiv  Detail & Related papers  (2025-04-20T19:05:59Z)
• On the Role of Feedback in Test-Time Scaling of Agentic AI Workflows [71.92083784393418]
  Agentic AI (systems that autonomously plan and act) are becoming widespread, yet their task success rate on complex tasks remains low.<n>Inference-time alignment relies on three components: sampling, evaluation, and feedback.<n>We introduce Iterative Agent Decoding (IAD), a procedure that repeatedly inserts feedback extracted from different forms of critiques.
  arXiv  Detail & Related papers  (2025-04-02T17:40:47Z)
• Zero-Shot LLMs in Human-in-the-Loop RL: Replacing Human Feedback for Reward Shaping [0.0]
  Reinforcement learning often faces challenges with reward misalignment.<n>Human-in-the-loop (HIL) methods may exacerbate the problem, as humans are prone to biases that lead to inconsistent, subjective, or misaligned feedback.
  arXiv  Detail & Related papers  (2025-03-26T03:17:12Z)
• ReviewAgents: Bridging the Gap Between Human and AI-Generated Paper Reviews [26.031039064337907]
  Academic paper review is a critical yet time-consuming task within the research community.<n>With the increasing volume of academic publications, automating the review process has become a significant challenge.<n>We propose ReviewAgents, a framework that leverages large language models (LLMs) to generate academic paper reviews.
  arXiv  Detail & Related papers  (2025-03-11T14:56:58Z)
• Reinforcement Learning with Segment Feedback [56.54271464134885]
  We consider a model named RL with segment feedback, which offers a general paradigm filling the gap between per-state-action feedback and trajectory feedback.<n>Under binary feedback, increasing the number of segments $m$ decreases the regret at an exponential rate; in contrast, surprisingly, under sum feedback, increasing $m$ does not reduce the regret significantly.
  arXiv  Detail & Related papers  (2025-02-03T23:08:42Z)
• AI-Driven Review Systems: Evaluating LLMs in Scalable and Bias-Aware Academic Reviews [18.50142644126276]
  We evaluate the alignment of automatic paper reviews with human reviews using an arena of human preferences by pairwise comparisons. We fine-tune an LLM to predict human preferences, predicting which reviews humans will prefer in a head-to-head battle between LLMs. We make the reviews of publicly available arXiv and open-access Nature journal papers available online, along with a free service which helps authors review and revise their research papers and improve their quality.
  arXiv  Detail & Related papers  (2024-08-19T19:10:38Z)
• REBEL: Reward Regularization-Based Approach for Robotic Reinforcement Learning from Human Feedback [61.54791065013767]
  A misalignment between the reward function and human preferences can lead to catastrophic outcomes in the real world.<n>Recent methods aim to mitigate misalignment by learning reward functions from human preferences.<n>We propose a novel concept of reward regularization within the robotic RLHF framework.
  arXiv  Detail & Related papers  (2023-12-22T04:56:37Z)
• UltraFeedback: Boosting Language Models with Scaled AI Feedback [99.4633351133207]
  We present textscUltraFeedback, a large-scale, high-quality, and diversified AI feedback dataset. Our work validates the effectiveness of scaled AI feedback data in constructing strong open-source chat language models.
  arXiv  Detail & Related papers  (2023-10-02T17:40:01Z)
• The Trickle-down Impact of Reward (In-)consistency on RLHF [71.37987812944971]
  We show that reward inconsistency exhibits a trickle-down effect on the downstream Reinforcement Learning from Human Feedback process. We propose Contrast Instructions -- a benchmarking strategy for the consistency of RM. We show that RLHF models trained with a more consistent RM yield more useful responses.
  arXiv  Detail & Related papers  (2023-09-28T04:05:13Z)
• Peering Through Preferences: Unraveling Feedback Acquisition for Aligning Large Language Models [32.843361525236965]
  We analyze the effect of sparse feedback on the alignment and evaluation of large language models. We find that preferences from ratings and rankings significantly disagree 60% for both human and AI annotators. Our findings shed light on critical gaps in methods for evaluating the real-world utility of language models.
  arXiv  Detail & Related papers  (2023-08-30T07:35:32Z)
• Dialogue Response Ranking Training with Large-Scale Human Feedback Data [52.12342165926226]
  We leverage social media feedback data to build a large-scale training dataset for feedback prediction. We trained DialogRPT, a set of GPT-2 based models on 133M pairs of human feedback data. Our ranker outperforms the conventional dialog perplexity baseline with a large margin on predicting Reddit feedback.
  arXiv  Detail & Related papers  (2020-09-15T10:50:05Z)

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.