3 citations · 3 across the 6 of their papers we have counts for
3 papers · 1 filter
Inference Auctions
Keegan Harris, Siddharth Prasad, Asher Trockman +2
When inference demand exceeds available compute capacity, model providers must decide which requests should be served first. Users have different tolerances for delay from an LLM A…
Post-Training at the Edge of Detectability: A Game-Theoretic Approach to Fine-Tuning
Keegan Harris, Brian W. Lee, Ian Waudby-Smith +3
Reinforcement learning (RL) fine-tuning is widely used in language model training to improve performance on a target task while limiting drift from a reference policy. A standard w…
In-Context Credit Assignment via the Core
Keegan Harris, Siddharth Prasad, Asher Trockman
We propose incentive-aligned mechanisms for in-context credit assignment: the task of assigning credit for AI-generated content among creators whose intellectual property appears i…