3 papers
cs.LG2026
Practical Scaling Laws: Converting Compute into Performance in a Data-Constrained World
Christopher M. Bryant, Hao Liu
The scaling laws guiding modern model training were calibrated for a single regime: data-rich, single-epoch pretraining. The dominant such scaling law form, Chinchilla's $L = E + A…
eess.SP2026
Inverse Design of Multi-Layer Sub-Pixel-Resolution RF Passives Through Grayscale Diffusion with Flexible S-Parameter Conditioning
Tommaso Dreossi, Christopher M. Bryant, Hao Liu +4
Inverse design of RF passive components from S-parameters is a high-dimensional, ill-posed problem, and prior generative approaches are limited to single-layer binary-metallization…
cs.CL2025
Reflect, Retry, Reward: Self-Improving LLMs via Reinforcement Learning
Shelly Bensal, Umar Jamil, Christopher Bryant +5
We explore a method for improving the performance of large language models through self-reflection and reinforcement learning. By incentivizing the model to generate better self-re…