Five ML Concepts - #24 | Software Wrighter Lab Blog

Five ML concepts in under 30 seconds each: Warmup (gradually increasing learning rate at start), Data Leakage (training on unavailable deployment info), Mode Collapse (limited generative output variety), Blue/Green Deployment (switching between parallel production environments), Reward Hacking (exploiting reward function flaws).

5 machine learning concepts. Under 30 seconds each.

Resource	Link
Papers	Links in References section
Video	Five ML Concepts #24

References

Concept	Reference
Warmup	Accurate, Large Minibatch SGD (Goyal et al. 2017)
Data Leakage	Leakage in Data Mining (Kaufman et al. 2012)
Mode Collapse	Generative Adversarial Nets (Goodfellow et al. 2014)
Blue/Green Deployment	MLOps best practice (no canonical paper)
Reward Hacking	Concrete Problems in AI Safety (Amodei et al. 2016)

Today’s Five

1. Warmup

Gradually increasing the learning rate at the start of training as part of a learning rate schedule. This helps stabilize early training when gradients can be noisy.

Warmup is especially important for large batch training.

Like stretching before a sprint instead of starting at full speed.

2. Data Leakage

When information unavailable at deployment accidentally influences model training. This creates artificially high validation scores that don’t reflect real-world performance.

Common sources include future data, preprocessing on full dataset, or duplicate samples.

Like memorizing test answers instead of learning the material.

3. Mode Collapse

When a generative model produces limited output diversity. The generator learns to produce only a few outputs that fool the discriminator.

A major challenge in GAN training that various architectures attempt to address.

Like a musician who only plays one song no matter the request.

4. Blue/Green Deployment

Maintaining two production environments and switching traffic between them. One serves live traffic while the other is updated and tested.

Enables instant rollback if problems occur.

Like having a backup stage ready so the show never stops.

5. Reward Hacking

When agents exploit reward functions in unintended ways. The agent optimizes the reward signal rather than the intended objective.

A key challenge in reinforcement learning and AI alignment.

Like gaming the grading rubric instead of learning the material.

Quick Reference

Concept	One-liner
Warmup	Gradually increasing learning rate at start
Data Leakage	Training on unavailable deployment info
Mode Collapse	Limited generative output variety
Blue/Green Deployment	Switching between parallel environments
Reward Hacking	Exploiting reward function flaws

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