4 papers
KARL: Knowledge Agents via Reinforcement Learning
Jonathan D. Chang, Andrew Drozdov, Shubham Toshniwal +23
We present a system for training enterprise search agents via reinforcement learning that achieves state-of-the-art performance across a diverse suite of hard-to-verify agentic sea…
A State-of-the-Art SQL Reasoning Model using RLVR
Alnur Ali, Ashutosh Baheti, Jonathan Chang +13
Developing custom reasoning models via Reinforcement Learning (RL) that can incorporate organization-specific knowledge has great potential to address problems faced by enterprise…
Beyond Chinchilla-Optimal: Accounting for Inference in Language Model Scaling Laws
Nikhil Sardana, Jacob Portes, Sasha Doubov +1
Large language model (LLM) scaling laws are empirical formulas that estimate changes in model quality as a result of increasing parameter count and training data. However, these fo…
Soup to go: mitigating forgetting during continual learning with model averaging
Anat Kleiman, Gintare Karolina Dziugaite, Jonathan Frankle +2
In continual learning, where task data arrives in a sequence, fine-tuning on later tasks will often lead to performance degradation on earlier tasks. This is especially pronounced…