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
Temporal-difference (TD) networks have been introduced as a formalism for expressing and learning grounded world knowledge in a predictive form (Sutton & Tanner, 2005). Like conventional TD(0) methods, the learning algorithm for TD networks uses 1-step backups to train prediction units about future events. In conventional TD learning, the TD(λ) algorithm is often used to do more general multi-step backups of future predictions. In our work, we introduce a generalization of the 1-step TD network specification that is based on the TD(λ) learning algorithm, creating TD(λ) networks. We present experimental results that show TD(λ) networks can learn solutions in more complex environments than TD networks. We also show that in problems that can be solved by TD networks, TD(λ) networks generally learn solutions much faster than their 1-step counterparts. Finally, we present an analysis of our algorithm that shows that the computational cost of TD(λ) networks is only slightly more than that of TD networks.
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 | 0.000 |
| 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