1 citations · 2 across the 8 of their papers we have counts for
5 papers · 1 filter
SPADE: Self-Play in Adaptive Synthetic Executable Environments
Bo Liu, Simon Yu, Yiding Jiang +15
Continuous self-improvement requires an ever-expanding pool of self-generated, diverse, adaptive goals. For language agents, existing training environment pools (hand-curated, stat…
PolySkill: Learning Generalizable Skills Through Polymorphic Abstraction
Simon Yu, Gang Li, Weiyan Shi +1
Large language models (LLMs) are moving beyond static uses and are now powering agents that learn continually during their interaction with external environments. For example, agen…
TextArena
Leon Guertler, Bobby Cheng, Simon Yu +3
TextArena is an open-source collection of competitive text-based games for training and evaluation of agentic behavior in Large Language Models (LLMs). It spans 57+ unique environm…
Diversify and Conquer: Diversity-Centric Data Selection with Iterative Refinement
Simon Yu, Liangyu Chen, Sara Ahmadian +1
Finetuning large language models on instruction data is crucial for enhancing pre-trained knowledge and improving instruction-following capabilities. As instruction datasets prolif…
Is It Good Data for Multilingual Instruction Tuning or Just Bad Multilingual Evaluation for Large Language Models?
Pinzhen Chen, Simon Yu, Zhicheng Guo +1
Multilingual large language models are designed, claimed, and expected to cater to speakers of varied languages. We hypothesise that the current practices of fine-tuning and evalua…