Bandit learning with regularized second-order mirror descent
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Many recent game-theoretic applications can benefit from relaxed assumptions on the players’ informational requirements as well as structural properties of the game. Bandit information represents one of the weakest possible environments for which convergence towards Nash equilibrium can be shown. Currently, most results on multi-agent bandit learning only consider games with strict monotonicity or strict variationally stable states. In this work, we propose a novel second-order variant of the bandit mirror descent algorithm and show that it can converge in games with mere VSS, which is a broader class of games compared to the ones studied in the existing literature. Aside from the incorporation of second-order learning, this convergence is also enabled through a Tikhonov regularization term. Furthermore, we show that our algorithm converges in representative games and the adjustment of the regularization term is consistent with our prediction.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 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 it