# RbtAct: Rebuttal as Supervision for Actionable Review Feedback Generation

arXiv: https://arxiv.org/abs/2603.09723 · HTML: https://mlsanity.com/paper/2603.09723/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in Human values and AI feedback alignment, one of 10 directions in LLM alignment / RLHF / preference optimisation.
- Builds on 25 of its 38 references that are in the map.
- First posted on arXiv 10 Mar 2026.

## Placed in

- LLM alignment / RLHF / preference optimisation / [Human values and AI feedback alignment](https://mlsanity.com/alignment/human-values-and-ai-feedback-alignment/)

## Builds on (most cited first, 25 of 25)

- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023)
- [DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models](https://mlsanity.com/paper/2512.02556/) (2025)
- [MinerU: An Open-Source Solution for Precise Document Content Extraction](https://mlsanity.com/paper/2409.18839/) (2024)
- [CycleResearcher: Improving Automated Research via Automated Review](https://mlsanity.com/paper/2411.00816/) (2024)
- [A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications](https://mlsanity.com/paper/1804.09635/) (2018)
- [ReviewerGPT? An Exploratory Study on Using Large Language Models for Paper Reviewing](https://mlsanity.com/paper/2306.00622/) (2023)
- [DeepReview: Improving LLM-based Paper Review with Human-like Deep Thinking Process](https://mlsanity.com/paper/2503.08569/) (2025)
- [AgentReview: Exploring Peer Review Dynamics with LLM Agents](https://mlsanity.com/paper/2406.12708/) (2024)
- [Can We Automate Scientific Reviewing?](https://mlsanity.com/paper/2102.00176/) (2021)
- [NLPeer: A Unified Resource for the Computational Study of Peer Review](https://mlsanity.com/paper/2211.06651/) (2022)
- [Peer Review as A Multi-Turn and Long-Context Dialogue with Role-Based Interactions](https://mlsanity.com/paper/2406.05688/) (2024)
- [GPT4 is Slightly Helpful for Peer-Review Assistance: A Pilot Study](https://mlsanity.com/paper/2307.05492/) (2023)
- [DISAPERE: A Dataset for Discourse Structure in Peer Review Discussions](https://mlsanity.com/paper/2110.08520/) (2021)
- [Automated Focused Feedback Generation for Scientific Writing Assistance](https://mlsanity.com/paper/2405.20477/) (2024)
- [MOPRD: A multidisciplinary open peer review dataset](https://mlsanity.com/paper/2212.04972/) (2022)
- [Mind the Blind Spots: A Focus-Level Evaluation Framework for LLM Reviews](https://mlsanity.com/paper/2502.17086/) (2025)
- [Re2: A Consistency-ensured Dataset for Full-stage Peer Review and Multi-turn Rebuttal Discussions](https://mlsanity.com/paper/2505.07920/) (2025)
- [SciArena: An Open Evaluation Platform for Non-Verifiable Scientific Literature-Grounded Tasks](https://mlsanity.com/paper/2507.01001/) (2025)
- [Can LLMs Identify Critical Limitations within Scientific Research? A Systematic Evaluation on AI Research Papers](https://mlsanity.com/paper/2507.02694/) (2025)
- [Exploring Jiu-Jitsu Argumentation for Writing Peer Review Rebuttals](https://mlsanity.com/paper/2311.03998/) (2023)
- [LimGen: Probing the LLMs for Generating Suggestive Limitations of Research Papers](https://mlsanity.com/paper/2403.15529/) (2024)
- [The Good, the Bad and the Constructive: Automatically Measuring Peer Review's Utility for Authors](https://mlsanity.com/paper/2509.04484/) (2025)
- [DIAGPaper: Diagnosing Valid and Specific Weaknesses in Scientific Papers via Multi-Agent Reasoning](https://mlsanity.com/paper/2601.07611/) (2026)

## Built on by (most cited first, 6 of 6)

- [AI for Auto-Research: Roadmap & User Guide](https://mlsanity.com/paper/2605.18661/) (2026)
- [Measuring the Gap Between Human and LLM Research Ideas](https://mlsanity.com/paper/2607.01233/) (2026)
- [Can AI Be a Good Peer Reviewer? A Survey of Peer Review Process, Evaluation, and the Future](https://mlsanity.com/paper/2604.27924/) (2026)
- [What Makes a Good AI Review? Concern-Level Diagnostics for AI Peer Review](https://mlsanity.com/paper/2604.19998/) (2026)
- [The Emerging AI Paper-Review Arms Race: Adversarial Co-Evolution in Scholarly Publishing](https://mlsanity.com/paper/2609.07713/) (2026)
- [ActReview: Rebuttal-Guided Training Data and Rubric Rewards for Actionable Peer Review Generation](https://mlsanity.com/paper/2609.09076/) (2026)

## Abstract (arXiv)

Large language models (LLMs) are increasingly used across the scientific workflow, including to draft peer-review reports. However, many AI-generated reviews are superficial and insufficiently actionable, leaving authors without concrete, implementable guidance and motivating the gap this work addresses. We propose RbtAct, which targets actionable review feedback generation and places existing peer review rebuttal at the center of learning. Rebuttals show which reviewer comments led to concrete revisions or specific plans, and which were only defended. Building on this insight, we leverage rebuttal as implicit supervision to directly optimize a feedback generator for actionability. To support this objective, we propose a new task called perspective-conditioned segment-level review feedback generation, in which the model is required to produce a single focused comment based on the complete paper and a specified perspective such as experiments and writing. We also build a large dataset named RMR-75K that maps review segments to the rebuttal segments that address them, with perspective labels and impact categories that order author uptake. We then train the Llama-3.1-8B-Instruct model with supervised fine-tuning on review segments followed by preference optimization using rebuttal derived pairs. Experiments with human experts and LLM-as-a-judge show consistent gains in actionability and specificity over strong baselines while maintaining grounding and relevance.