Real-Time Adaptive Loss Functions for Generative Models Using Reinforcement Learning and Meta-Learning
Bibliographic record
Abstract
This paper presents a novel approach to training generative models using loss functions that adapt in real time via a meta-learning controller while optionally incorporating a reinforcement-learning (RL) based monitor. Comprehensive ablation studies on a SimpleCNN trained on a 5-image per-class CIFAR-10 subset show that the Meta-Controller-Only configuration yields the best validation accuracy (61.5 %), outperforming a static cross-entropy baseline (60.7 %) by 0.8 percentage points (≈ 1.3 % relative). In contrast, the RL Monitor-Only setting degrades performance (60.4 %), and combining both agents erodes the Meta-Controller's gains due to negative interference (60.6 %). These findings indicate that the learned metarules , rather than the RL policy, are primarily responsible for generalization improvements, and that naïvely fusing the two mechanisms can be counter-productive. The paper details the system architecture, safety mechanisms, experimental protocol, and a critical discussion of the component-wise results and their implications for future adaptive-loss research.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".