Sarath Chandar Anbil Parthipan
, Khimya Khetarpal, Janarthanan Rajendran and Matthew Riemer
Paper (2024)
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| Department: | Department of Computer Engineering and Software Engineering |
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| ISBN: | 9798331314385 |
| PolyPublie URL: | https://publications.polymtl.ca/73398/ |
| Conference Title: | 38th Conference on Neural Information Processing Systems (NeurIPS 2024) |
| Conference Location: | Vancouver, BC, Canada |
| Conference Date(s): | 2024-12-10 - 2024-12-15 |
| Publisher: | Neural Information Processing Systems Foundation, Inc. (NeurIPS) |
| DOI: | 10.52202/079017-2552 |
| Official URL: | https://doi.org/10.52202/079017-2552 |
| Date Deposited: | 09 Apr 2026 10:41 |
| Last Modified: | 09 Apr 2026 10:41 |
| Cite in APA 7: | Anbil Parthipan, S. C., Khetarpal, K., Rajendran, J., & Riemer, M. (2024, December). Balancing Context Length and Mixing Times for Reinforcement Learning at Scale [Paper]. 38th Conference on Neural Information Processing Systems (NeurIPS 2024), Vancouver, BC, Canada. https://doi.org/10.52202/079017-2552 |
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