Learning predictive state representations using non-blind policies
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
Predictive state representations (PSRs) are powerful models of non-Markovian decision processes that differ from traditional models (e.g., HMMs, POMDPs) by representing state using only observable quantities. Because of this, PSRs can be learned solely using data from interaction with the process. The majority of existing techniques, though, explicitly or implicitly require that this data be gathered using a blind policy, where actions are selected independently of preceding observations. This is a severe limitation for practical learning of PSRs. We present two methods for fixing this limitation in most of the existing PSR algorithms: one when the policy is known and one when it is not. We then present an efficient optimization for computing good exploration policies to be used when learning a PSR. The exploration policies, which are not blind, significantly lower the amount of data needed to build an accurate model, thus demonstrating the importance of non-blind policies. 1.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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