Evaluating state-space abstractions in extensive-form games
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
Efficient algorithms exist for finding optimal policies in extensive-form games. However, human-scale problems are typically so large that this computation remains infeasible with modern computing resources. State-space abstraction techniques allow for the derivation of a smaller and strategically similar abstract domain, in which an optimal strategy can be computed and then used as a suboptimal strategy in the real domain. In this paper, we consider the task of evaluating the quality of an abstraction, independent of a specific abstract strategy. In particular, we use a recent metric for abstraction quality and examine imperfect recall abstractions, in which agents “forget ” previously observed information to focus the abstraction effort on more recent and relevant state information. We present experimental results in the domain of Texas hold’em poker that validate the use of distribution-aware abstractions over expectation-based approaches, demonstrate that the new metric better predicts tournament performance, and show that abstractions built using imperfect recall outperform those built using perfect recall in terms of both exploitability and one-on-one play.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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