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Human Feedback and Eval Paper Explorer

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Total papers: 5 Search mode: keyword Shortlist (0) RSS

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Elo-Evolve: A Co-evolutionary Framework for Language Model Alignment

Jing Zhao, Ting Zhen, Junwei Bao, Hongfei Jiang, Yang Song · Feb 14, 2026

Citations: 0

Match reason: Keyword overlap 1/1 across title and protocol fields.

Score: 83% High protocol signal Freshness: Warm Status: Fallback
Pairwise Preference Automatic Metrics Multi Agent General
  • Current alignment methods for Large Language Models (LLMs) rely on compressing vast amounts of human preference data into static, absolute reward functions, leading to data scarcity, noise sensitivity, and training instability.
  • We introduce Elo-Evolve, a co-evolutionary framework that redefines alignment as dynamic multi-agent competition within an adaptive opponent pool.
Open paper
PIKA: Expert-Level Synthetic Datasets for Post-Training Alignment from Scratch

Shangjian Yin, Shining Liang, Wenbiao Ding, Yuli Qian, Zhouxing Shi, Hongzhi Li · Oct 8, 2025

Citations: 0

Match reason: Keyword overlap 1/1 across title and protocol fields.

Score: 75% Moderate protocol signal Freshness: Cold Status: Fallback
Pairwise Preference Coding
  • Despite its small size, fine-tuning Llama-3-8B-Base on PiKa-SFT even outperforms the official Llama-3-8B-Instruct model trained on over 10M proprietary examples on widely used benchmarks such as AlpacaEval 2.0 and Arena-Hard.
  • Additionally, we provide 30k high-quality preference optimization examples to further enhance alignment.
Open paper
Citations: 0

Match reason: Keyword overlap 1/1 across title and protocol fields.

Score: 75% Moderate protocol signal Freshness: Cold Status: Fallback
Pairwise Preference Coding
  • To bridge this gap, we propose Meta-Weighted Adaptive Preference Optimization (MetaAPO), a novel framework that dynamically couples data generation with model training.
  • Experiments on AlpacaEval 2, Arena-Hard and MT-Bench demonstrate that MetaAPO consistently outperforms existing preference optimization approaches across various settings, while reducing 42% in online annotation costs.
Open paper
Towards Bridging the Reward-Generation Gap in Direct Alignment Algorithms

Zeguan Xiao, Yun Chen, Guanhua Chen, Ke Tang · Jun 11, 2025

Citations: 0

Match reason: Keyword overlap 1/1 across title and protocol fields.

Score: 75% Moderate protocol signal Freshness: Cold Status: Fallback
Pairwise Preference General
  • Direct Alignment Algorithms (DAAs), such as Direct Preference Optimization (DPO) and Simple Preference Optimization (SimPO), have emerged as efficient alternatives to Reinforcement Learning from Human Feedback (RLHF) algorithms for aligning…
Open paper
Less is More: Improving LLM Alignment via Preference Data Selection

Xun Deng, Han Zhong, Rui Ai, Fuli Feng, Zheng Wang, Xiangnan He · Feb 20, 2025

Citations: 0

Match reason: Keyword overlap 1/1 across title and protocol fields.

Score: 75% Moderate protocol signal Freshness: Cold Status: Fallback
Pairwise Preference General
  • Direct Preference Optimization (DPO) has emerged as a promising approach for aligning large language models with human preferences.
  • To further mitigate the noise in different reward models, we propose a Bayesian Aggregation approach that unifies multiple margin sources (external and implicit) into a single preference probability.
Open paper

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