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cs.LG2025
CodeMonkeys: Scaling Test-Time Compute for Software Engineering
Ryan Ehrlich, Bradley Brown, Jordan Juravsky +3
Scaling test-time compute is a promising axis for improving LLM capabilities. However, test-time compute can be scaled in a variety of ways, and effectively combining different app…
cs.LG2024★ 12 cited
Large Language Monkeys: Scaling Inference Compute with Repeated Sampling
Bradley Brown, Jordan Juravsky, Ryan Ehrlich +4
Scaling the amount of compute used to train language models has dramatically improved their capabilities. However, when it comes to inference, we often limit models to making only…
cs.LG2024
Hydragen: High-Throughput LLM Inference with Shared Prefixes
Jordan Juravsky, Bradley Brown, Ryan Ehrlich +3
Transformer-based large language models (LLMs) are now deployed to hundreds of millions of users. LLM inference is commonly performed on batches of sequences that share a prefix, s…