A Novel way of Training a Neural Network with Reinforcement learning and without Back Propagation
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Bibliographic record
Abstract
This paper introduces, explores and shows a new way to optimize the weights of a neural network. This new technique does not use back propagation or relies on gradient descent. The core idea is to combine both Game Theory and Reinforcement Learning to train a Neural network. We structure a game of learning automata agents, specifically Continuous Action Learning Automata (CALA) agents, that iteratively converges to a global minimum. Each CALA agent is associated with a weight in a neural network, and when the game converges to a global minimum, we can say that the neural network has been trained. By using a game of CALA agents we can deal with input that has been corrupted by noise, do not have to worry about the vanishing gradient problem or worry about over fitting and is structured better for parallel computing.
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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