From the 1 of 16 linked papers with an AI index.
16 papers
Test-Time Learning with an Evolving Library
Weijia Xu, Alessandro Sordoni, Chandan Singh +4
The paper introduces EvoLib, a test-time learning framework that lets large language models build, reuse, and evolve a shared library of knowledge abstractions across tasks without…
Putting the Value Back in RL: Better Test-Time Scaling by Unifying LLM Reasoners With Verifiers
Kusha Sareen, Morgane M Moss, Alessandro Sordoni +2
Prevalent reinforcement learning~(RL) methods for fine-tuning LLM reasoners, such as GRPO or Leave-one-out PPO, abandon the learned value function in favor of empirically estimated…
Trade-offs in Ensembling, Merging and Routing Among Parameter-Efficient Experts
Sanae Lotfi, Lucas Caccia, Alessandro Sordoni +2
While large language models (LLMs) fine-tuned with lightweight adapters achieve strong performance across diverse tasks, their performance on individual tasks depends on the fine-t…
Learning to Solve Complex Problems via Dataset Decomposition
Wanru Zhao, Lucas Caccia, Zhengyan Shi +3
Curriculum learning is a class of training strategies that organizes the data being exposed to a model by difficulty, gradually from simpler to more complex examples. This research…
MedRiskEval: Medical Risk Evaluation Benchmark of Language Models, On the Importance of User Perspectives in Healthcare Settings
Jean-Philippe Corbeil, Minseon Kim, Maxime Griot +4
As the performance of large language models (LLMs) continues to advance, their adoption in the medical domain is increasing. However, most existing risk evaluations largely focused…
Effect of Document Packing on the Latent Multi-Hop Reasoning Capabilities of Large Language Models
Gabriele Prato, Shagun Sodhani, Alessandro Sordoni +1
The standard practice for training large language models involves packing multiple documents together to optimize computational efficiency. However, the impact of this process on t…