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About the Lab
Chandar Research Lab (CRL) is a machine learning research group in the Department of Computer and Software Engineering at Polytechnique Montreal. The long-term mission of our group is to develop interactive learning algorithms that continuously learn from experiences. Currently, the lab focuses on several areas in machine learning including Deep Learning, Reinforcement Learning, Lifelong Learning, and Natural Language Processing.
CRL is also affiliated with Mila, the Quebec AI Institute.
News
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(September 2025) We’ll be having our annual CRL Symposium 2025 on August 19-20. Come check it out!
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(August 2025) Two works on RLHF and representation steering accepted to COLM 2025.
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(July 2025) One work on point tracking accepted to ICCV 2025.
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(June 2025) One paper accepted to ACL 2025 and two more accepted to Findings.
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(June 2025) Two works accepted to CoLLAs 2025.
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(February 2025) Our work on generalization in multi-agent Hanabi accepted to ICLR 2025.
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( 2024) One work on building LLMs for 3D molecule generation accepted to AAAI 2025.
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(November 2024) One work on context length in reinforcement learning accepted to NeurIPS.
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(October 2024) Two papers on knowledge understanding in LLMs and speculative decoding accepted to EMNLP and another on LLM quantization accepted to Findings.
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(September 2024) One work on attention modulation for fairness accepted to COLM.
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(June 2024) Our work on self-explanations from large language models was accepted at Findings of ACL 2024.
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(June 2024) Two papers accepted at ACL 2024: prompt-based fairness metrics correlation and parameter-efficient preference alignment.
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(June 2024) Two papers accepted at ICML 2024: faithfulness measurable masked language models and Lookbehind-SAM.
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(May 2024) Our work on sub-goal distillation was accepted at CoLLAs 2024.
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(February 2024) Two papers accepted at ICLR 2024: intelligent switching in reset-free RL and mastering memory tasks with world models.
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(February 2024) Our work on offline reinforcement learning for crystal design was accepted at Digital Discovery.
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(February 2024) Our work on fairness-aware pruning in Transformers was accepted at AAAI 2024.
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(November 2023) Three papers accepted at EMNLP 2023: knowledge acquisition-utilization gap in LLMs, evaluation of LLMs, and data reweighting for LLMs.
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(June 2023) Our work on cooperative multi-agent reinforcement learning was accepted at UAI 2023.
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(June 2023) Two papers accepted at CoLLAs 2023: adaptive model-based reinforcement learning and few-shot coordination in Hanabi.