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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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 source (direct Gemma or distilled Codex), 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".