arXiv Machine Learning
Sep 7

Consensus Group Relative Policy Optimization for Text Generation

Consensus Group Relative Policy Optimization (C‑GRPO) is a new training method that distills Minimum Bayes Risk (MBR) decoding into a group‑relative objective, enabling text generation models to approximate MBR performance without the costly inference‑time sampling and scoring. C‑GRPO only needs a utility function and policy samples, avoiding the need for gold references or curated preference data. Experiments on WMT 2024 machine translation and XSum summarization show that C‑GRPO matches MBR decoding quality while reducing inference overhead and outperforming other reference‑free baselines.

By Yuki Ichihara, Yuu Jinnai, Kaito Ariu, Eiji Uchibe
arXiv AI
2d ago

R3: Robust Rubric-Agnostic Reward Models

R3 is a new reward modeling framework that addresses limitations in current reward models by being rubric‑agnostic and generalizable across multiple evaluation dimensions. It provides interpretable, reasoned score assignments rather than opaque scalar outputs, enhancing transparency and flexibility in evaluating language models. The authors release their models, data, and code openly at https://github.com/rubricreward/r3.

By David Anugraha, Zilu Tang, Lester James V. Miranda, Hanyang Zhao, Mohammad Rifqi Farhansyah, Garry Kuwanto, Derry Wijaya, Genta Indra Winata
Hugging Face Trending Papers
Jun 3

BiasGRPO: Stabilizing Bias Mitigation in High-Variance Reward Landscapes via Group-Relative Policy Optimization

Mitigating social bias in Large Language Models (LLMs) presents a distinct alignment challenge: unlike verifiable tasks, bias lacks a single ground truth, creating a high-variance, subjective reward landscape. Previous preference-based fine-tuning methods have major trade-offs: Direct Preference Optimization (DPO) is limited by the lack of exploration inherent in offline training, while Proximal Policy Optimization (PPO) can lead to training instability due to potentially unreliable critic estimates.

arXiv Machine Learning
Aug 19

GUPO: Gradient Uncertainty-aware Policy Optimization for Post-Training Large Language Models

GUPO: Gradient Uncertainty-aware Policy Optimization for Post-Training Large Language Models proposes a new method for fine‑tuning LLMs after training. The approach models each group gradient as a random variable, estimates its probability distribution, and uses Dirichlet‑based gradient uncertainty to weight each group’s contribution during policy updates. Experiments on multiple benchmarks show that this uncertainty‑aware aggregation improves the effectiveness of post‑training policy optimization.

By Peizheng Guo, Jianqi Zhang, Xingyu Zhang, Yun Fan, Jiahuan Zhou, Changwen Zheng, Wenwen Qiang