Baseline: practical control variates for agent evaluation in zero-sum domains
Bibliographic record
Abstract
Agent evaluation in stochastic domains can be difficult. The commonplace approach of Monte Carlo evaluation can in-volve a prohibitive number of simulations when the variance of the outcome is high. In such domains, variance reduc-tion techniques are necessary, but these techniques require careful encoding of domain knowledge. This paper intro-duces baseline as a simple approach to creating low vari-ance estimators for zero-sum multi-agent domains with high outcome variance. The baseline method leverages the self play of any available agent to produce a control variate for variance reduction, subverting any extra complexity inher-ent with traditional approaches. The baseline method is also applicable in situations where existing techniques either require extensive implementation overhead or simply can-not be applied. Experimental variance reduction results are shown for both cases using the baseline method. Baseline is shown to surpass state-of-the-art techniques in three-player computer poker and is competitive in two-player computer poker games. Baseline also shows variance reduction in hu-man poker and in a mock Ad Auction tournament from the Trading Agent Competition, domains where variance reduc-tion methods are not typically employed.
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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 | 0.002 | 0.002 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".