Exogenous control over model training is a significant one, particularly in the context of adaptive learning systems. Ideally, a model should be able to self-monitor its performance and trigger retraining cycles when necessary, rather than relying on external intervention. Here are some strategies to address this problem:

1. Self-Monitoring and Adaptive Retraining: Implement mechanisms within the model that allow it to assess its own performance against predefined metrics or benchmarks. If the model detects a drop in performance or a mismatch between its predictions and actual outcomes, it could initiate a retraining process.

2. Continuous Learning: Instead of periodic retraining, the model could be designed to learn continuously from new data. This approach, often referred to as online learning or incremental learning, allows the model to adapt in real-time as new information becomes available.

3. Automated Feature Extraction: The model could be equipped with the ability to identify and extract new features from the data that are relevant to the task at hand. This would enable the model to adapt to changes in the data distribution or to new patterns that emerge over time.

4. Meta-Learning: Incorporate meta-learning techniques that enable the model to learn how to learn. This could involve adjusting the learning rate, batch size, or other hyperparameters dynamically based on the model's performance.

5. Anomaly Detection: Implement anomaly detection algorithms that can identify when the model's performance is deviating from expected norms. This could serve as a trigger for retraining when the model encounters data that is significantly different from what it was trained on.

6. Reinforcement Learning: Use reinforcement learning to allow the model to optimize its own retraining schedule. The model could be rewarded for maintaining high performance and penalized for overfitting or underfitting, leading it to learn the optimal retraining strategy.

7. Human-in-the-Loop: While the goal is to make the model more autonomous, incorporating a human-in-the-loop can provide a safety net. Human experts can review the model's self-assessment and intervene if necessary, ensuring that the model's self-training decisions are aligned with organizational goals and ethical considerations.

8. Data Drift Detection: Monitor for changes in the data distribution over time, a phenomenon known as concept drift or data drift. When significant drift is detected, the model can be retrained to ensure it remains relevant and accurate.

9. Model Explainability: Enhance the model's explainability to understand why it is making certain decisions about when to retrain. This transparency can help in validating the model's self-training logic and in making adjustments if needed.

10. Experimentation and A/B Testing: Implement an experimentation framework that allows for A/B testing of different retraining strategies. This can help in empirically determining the most effective approach for the model to self-train.