3 papers
cs.LG2025
Compute-Optimal Scaling for Value-Based Deep RL
Preston Fu, Oleh Rybkin, Zhiyuan Zhou +4
As models grow larger and training them becomes expensive, it becomes increasingly important to scale training recipes not just to larger models and more data, but to do so in a co…
cs.LG2025
Value-Based Deep RL Scales Predictably
Oleh Rybkin, Michal Nauman, Preston Fu +4
Scaling data and compute is critical to the success of modern ML. However, scaling demands predictability: we want methods to not only perform well with more compute or data, but a…
cs.LG2024
Predicting Emergent Capabilities by Finetuning
Charlie Snell, Eric Wallace, Dan Klein +1
A fundamental open challenge in modern LLM scaling is the lack of understanding around emergent capabilities. In particular, language model pretraining loss is known to be highly p…