Regularized Reinforcement Learning with Performance Guarantees
Bibliographic record
Abstract
To my wife, parents and supporting friends. ii ACKNOWLEDGEMENTS I would like to thank all members of McGill’s Reasoning and Learning lab who provided me with useful thoughts and ideas throughout my graduate studies. I am particularly thankful to my supervisor, Joelle Pineau, for her relentless help and support over this time. I would also like to thank Doina Precup for her invaluable contributions to my research and studies at McGill University. Special thanks goes to Yuri Grinberg, Amir-massoud Farahmand and Csaba Szepesvári for their contributions to my research and publications. I am also thankful to Pierre-Luc Bacon for his help in writing the French abstract, and Angus Leigh for being an awesome lab-mate. iii Reinforcement learning covers a broad category of control problems in which
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".