3 papers
cs.LG2026
Failed Reasoning Traces Tell You What Is Fixable (But Not by Reading Them)
Nizar Islah, Istabrak Abbes, Irina Rish +2
When post-trained language models fail on reasoning problems, the common test-time-scaling response is to spend more compute on additional attempts, and the failed traces play no f…
cs.LG2025
Revisiting Replay and Gradient Alignment for Continual Pre-Training of Large Language Models
Istabrak Abbes, Gopeshh Subbaraj, Matthew Riemer +6
Training large language models (LLMs) typically involves pre-training on massive corpora, only to restart the process entirely when new data becomes available. A more efficient and…
cs.CL2025
Small Encoders Can Rival Large Decoders in Detecting Groundedness
Istabrak Abbes, Gabriele Prato, Quentin Fournier +4
Augmenting large language models (LLMs) with external context significantly improves their performance in natural language processing (NLP) tasks. However, LLMs struggle to answer…