Integrating Representation Learning and Temporal Difference Learning: A Matrix Factorization Approach
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Bibliographic record
Abstract
Reinforcement learning is a general formalism for sequential decision-making, with recent algorithm development focusing on function approximation to handle large state spaces and high-dimensional, high-velocity (sensor) data. The success of function approximators, however, hinges on the quality of the data representation. In this work, we explore representation learning within least-squares temporal difference learning (LSTD), with a focus on making the assumptions on the representation explicit and making the learning problem amenable to principled optimization techniques. We reformulate LSTD as a least-squares loss plus concave regularizer, facilitating the addition of a regularized matrix factorization objective to specify the desired class of representations. The resulting joint optimization over the representation and value function parameters enables us to take advantages of recent advances in unsupervised learning and presents a general yet simple formalism for learning representations in reinforcement learning.
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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