Implicit incremental natural actor critic algorithm
Why this work is in the frame
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Bibliographic record
Abstract
Natural policy gradient (NPG) methods are promising approaches to finding locally optimal policy parameters. The NPG approach works well in optimizing complex policies with high-dimensional parameters, and the effectiveness of NPG methods has been demonstrated in many fields. However, the incremental estimation of the NPG is computationally unstable owing to its high sensitivity to the step-sizes values, especially to the one used to update the estimate of NPG. In this study, we propose a new incremental and stable algorithm for the NPG estimation. We call the proposed algorithm the implicit incremental natural actor critic (I2NAC), and it is based on the idea of the implicit update. The convergence analysis for I2NAC is provided. Theoretical analysis results indicate the stability of I2NAC and the instability of conventional incremental NPG methods. Numerical experiments were performed, and the results show that I2NAC is less sensitive to the values of the meta-parameters, including the step-size for the NPG update, compared to the existing incremental NPG method.
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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 | 0.001 | 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