1 citations · 1 across the 6 of their papers we have counts for
6 papers
Behaviorally Grounded User Profiles from the Wild for Personalized Alignment and Multi-Perspective Reasoning
Yuxuan Li, Victor Zhong, Ehsan Kamalloo
Persona-driven techniques increasingly adapt large language models (LLMs) to diverse contexts. However, existing methods predominantly rely on rigid, synthetic personas that flatte…
Apriel-Reasoner: RL Post-Training for General-Purpose and Efficient Reasoning
Rafael Pardinas, Ehsan Kamalloo, David Vazquez +1
Building general-purpose reasoning models using reinforcement learning with verifiable rewards (RLVR) across diverse domains has been widely adopted by frontier open-weight models.…
PipelineRL: Faster On-policy Reinforcement Learning for Long Sequence Generation
Alexandre Piché, Ehsan Kamalloo, Rafael Pardinas +2
Reinforcement Learning (RL) is increasingly utilized to enhance the reasoning capabilities of Large Language Models (LLMs). However, effectively scaling these RL methods presents s…
LongRecall: A Structured Approach for Robust Recall Evaluation in Long-Form Text
MohamamdJavad Ardestani, Ehsan Kamalloo, Davood Rafiei
LongRecall. The completeness of machine-generated text, ensuring that it captures all relevant information, is crucial in domains such as medicine and law and in tasks like list-ba…
Self-Evolving Curriculum for LLM Reasoning
Xiaoyin Chen, Jiarui Lu, Minsu Kim +6
Reinforcement learning (RL) has proven effective for fine-tuning large language models (LLMs), significantly enhancing their reasoning abilities in domains such as mathematics and…
TapeAgents: a Holistic Framework for Agent Development and Optimization
Dzmitry Bahdanau, Nicolas Gontier, Gabriel Huang +10
We present TapeAgents, an agent framework built around a granular, structured log tape of the agent session that also plays the role of the session's resumable state. In TapeAgents…