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The recent rise of Large Reasoning Models (LRMs) has significantly improved multi-step reasoning performance, but often at the cost of generating excessively long reasoning chains.
A mathematical theory of communication
Claude E Shannon · 1948
Earlier work this paper cites.
Human problem solving: The state of the theory in 1970
Herbert A Simon and Allen Newell · 1971
Earlier work this paper cites.
Act-r: A theory of higher level cognition and its relation to visual attention
John R Anderson, Michael Matessa, and Christian Lebiere · 1997
Earlier work this paper cites.
Unsupervised document classification using sequential information maximization
Noam Slonim, Nir Friedman, and Naftali Tishby · 2002
Earlier work this paper cites.
In two minds: dual-process accounts of reasoning
Jonathan St BT Evans · 2003
Earlier work this paper cites.
Estimation of entropy and mutual information
Liam Paninski · 2003
Earlier work this paper cites.
Thinking, fast and slow
Daniel Kahneman · 2011
Earlier work this paper cites.
Understanding act-r-an outsider’s perspective
Jacob Whitehill · 2013
Earlier work this paper cites.
Controlling bias in adaptive data analysis using information theory
Daniel Russo and James Zou · 2016
Earlier work this paper cites.
Information-theoretic analysis of generalization capability of learning algorithms
Aolin Xu and Maxim Raginsky · 2017
Earlier work this paper cites.
Commonsenseqa: A question answering challenge targeting commonsense knowledge
Alon Talmor, Jonathan Herzig, Nicholas Lourie, and Jonathan Berant · 2018
Earlier work this paper cites.
Peter West, Ari Holtzman, Jan Buys, and Yejin Choi · 2019
Earlier work this paper cites.
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Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
Earlier work this paper cites.
Measuring massive multitask language understanding
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt · 2020
Earlier work this paper cites.
Scaling laws for neural language models
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei · 2020
Earlier work this paper cites.
Neural-symbolic learning and reasoning: A survey and interpretation 1
Tarek R Besold, Sebastian Bader, Howard Bowman, Pedro Domingos, Pascal Hitzler, Kai-Uwe Kühnberger, Luis C Lamb, Priscila Machado Vieira Lima, Leo de Penning, Gadi Pinkas, et al · 2021
Earlier work this paper cites.
Training verifiers to solve math word problems
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, et al · 2021
Earlier work this paper cites.
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Earlier work this paper cites.
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