World PulseNow
Newsletter

Foundational Automatic Evaluators: Scaling Multi-Task Generative Evaluator Training for Reasoning-Centric Domains

arXiv — cs.LG•Thursday, November 20, 2025 at 5:00:00 AM
PositiveArtificial Intelligence
  • The development of Foundational Automatic Reasoning Evaluators (FARE) marks a significant advancement in the training of generative evaluators, focusing on scalability and performance in reasoning tasks.
  • This initiative is crucial as it addresses the growing demand for effective evaluation methods in artificial intelligence, particularly in complex reasoning scenarios, enhancing the capabilities of existing models.
  • The introduction of FARE aligns with ongoing efforts in the AI community to tackle challenges such as mode collapse in large language models, emphasizing the importance of diverse and robust evaluation techniques.
— via World Pulse Now AI Editorial System
Read Original

Was this article worth reading? Share it

Recommended Readings
Group-Aware Reinforcement Learning for Output Diversity in Large Language Models
arXiv — cs.LG2 days ago
Group-Aware Reinforcement Learning for Output Diversity in Large Language Models
PositiveArtificial Intelligence
Large Language Models (LLMs) often experience mode collapse, generating limited responses despite the availability of diverse answers. To address this issue, researchers have introduced Group-Aware Policy Optimization (GAPO), an extension of Group Relative Policy Optimization (GRPO). GAPO focuses on group-level properties such as diversity and coverage, utilizing a frequency-aware reward function to promote uniform sampling of valid completions. The results indicate that models trained with GAPO yield more varied and valid responses while maintaining accuracy across standard benchmarks like GS…
Read full article
via arXiv — cs.LG