1 citations · 1 across the 4 of their papers we have counts for
4 papers · 1 filter
Alignment by Confident Debate: Preference Learning from Multi-Agent Debate
Nikhil Verma, Dingxian Wang, Mohammad Reza Rezaei +2
Post-training language models for reasoning typically rely on gold answers, task-specific verifiers, or learned reward models, limiting scalability when such signals are scarce. Mu…
PAYWORTHY: Can LLMs Predict Which Freelance Jobs an AI Agent Will Complete?
Mohammad Reza Rezaei, Dingxian Wang, Brett Levert +3
Before an AI agent takes on a paid job, someone has to judge whether it is likely to succeed. We test whether frontier language models can make this call before any attempt. We pro…
Online Label-Free Gradient Matching for Continual Language Model Training
Sajjad Pakdamansavoji, Mohammad Reza Rezaei, Ilija Subasic +3
Language models in customer-facing products must be continually updated, since the data they serve shifts over time. As retraining on the full accumulated data is impractical, each…
Fragility Is Not Reducible to Hardness: Conditional Causal Difficulty for LLMs
Mohammad Reza Rezaei, Dingxian Wang, Nikhil Verma +3
Large language models show large and seemingly arbitrary performance drops when a task is rewritten in ways that leave its requirements untouched. We recast task difficulty as a ca…