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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".