Showing cs.LGShow all
3 papers · 1 filter
cs.LG2026
DeliveryGym: An RL Environment for Long-Horizon Embodied Agent Planning with Adaptive Curriculum
Haoqiang Kang, Yiming Zhang, Yiyang Guo +5
Executable environments enable LLM agents to learn from the consequences of their actions. For embodied agents, those consequences extend beyond whether the current task succeeds:…
cs.LG2026
MOSAIC: More LLMs, Less Cost through Collaborative Reasoning for Compiler Optimizations
Annabelle Sujun Tang, Christopher Priebe, Lianhui Qin +1
LLM-guided compiler optimization has recently shown promise. However, current LLM-guided optimizers query a single LLM at every search step, so their cost grows with both that mode…
cs.LG2025
REASONING COMPILER: LLM-Guided Optimizations for Efficient Model Serving
Annabelle Sujun Tang, Christopher Priebe, Rohan Mahapatra +2
While model serving has unlocked unprecedented capabilities, the high cost of serving large-scale models continues to be a significant barrier to widespread accessibility and rapid…