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data bias 1generation diversity 1large language models 1mode collapse 1prompt engineering 1

From the 1 of 11 linked papers with an AI index.

most citedVerbalized Sampling: How to Mitigate Mode Collapse and Unlock LLM Diversity

2 citations · 2 across the 11 of their papers we have counts for

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cs.LG2026

RASFT: Rollout-Adaptive Supervised Fine-Tuning for Reasoning

Yongliang Miao, Fengyuan Liu, Wei Shi +4

Supervised fine-tuning (SFT) is a prevailing method for adapting large language models to reasoning tasks by imitating offline expert demonstrations, often treating a single expert…

cs.LG2026

GEM: A Gym for Agentic LLMs

Zichen Liu, Anya Sims, Keyu Duan +16

The training paradigm for large language models (LLMs) is moving from static datasets to experience-based learning, where agents acquire skills via interacting with complex environ…