Online bandit non-cooperative games with arbitrary delays
Why this work is in the frame
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Bibliographic record
Abstract
This paper considers online bandit games with arbitrary delays, where the cost functions of all self-interested players are time-varying. In addition, players lack an explicit model of the game and can only learn their actions based on the sole available feedback of delayed cost values. To address this challenging setting, a novel learning algorithm named Cumulative Bandit Online Learning with arbitrary delays (CBOL-ad) is proposed. We conduct regret analysis for time-varying games where the player-specific problem is convex, explicitly revealing the influence of time delays and game structure on the regret bound. In particular, under certain delay conditions, our bound can achieve the same order as that of online bandit optimization problems without delays. Finally, numerical simulations are provided to illustrate the algorithmic performance.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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