Doubly Optimal No-Regret Online Learning in Strongly Monotone Games with Bandit Feedback
Why this work is in the frame
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Bibliographic record
Abstract
Doubly Optimal No-Regret Online Learning in Strongly Monotone Games with Bandit Feedback Curious about how players can learn and adapt in unknown games without knowing the game’s dynamics? In “Doubly Optimal No-Regret Online Learning in Strongly Monotone Games with Bandit Feedback,” Ba, Lin, Zhang, and Zhou present a novel bandit learning algorithm for no-regret learning in games where each player only observes its reward determined by all players’ current joint action, not its gradient. Focusing on smooth and strongly monotone games, they introduce a bandit learning algorithm using self-concordant barrier functions. This algorithm achieves optimal single-agent regret and optimal last-iterate convergence rate in multiagent learning to the Nash equilibrium. Their work significantly improves previous methods and demonstrates the algorithm’s effectiveness through numerical results in various applications.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 it