Why Adam Optimizer Fails in RL and Transformers

Adam is often treated as a "set it and forget it" solution, but relying on it blindly can lead to some absolute nightmares during training. I've been hitting a wall with my Reinforcement Learning (RL) projects where the loss curves aren't just noisy—they're exhibiting this erratic "burstiness" that makes the whole model feel unstable. You'll see a decent trend, and then suddenly the loss spikes for no apparent reason, nearly blowing up the gradients.

For a while, I thought my reward function was the culprit or that my hyperparameters were just slightly off. But after doing a deep dive into the mechanics of how Adam handles adaptive learning rates, the picture became clearer. The way Adam tracks the first and second moments of the gradients can actually lead to instability in non-stationary environments, which is exactly what happens in RL or when training massive deep transformers.

The Diagnosis: Why the "Burstiness" Happens

When you're dealing with RL, the data distribution shifts constantly as the agent learns. Adam's reliance on a moving average of squared gradients (the second moment) means it's essentially trying to normalize the update based on past behavior. If the agent hits a region of the state space with radically different gradient magnitudes, the denominator in the Adam update rule can become problematic.

The result isn't always a total crash, but it creates these unpredictable jumps in the loss. It's less of a smooth descent and more of a jagged crawl. If you're seeing your loss values jump sporadically despite a low learning rate, you're likely fighting the optimizer, not the model architecture.

Practical AI Workflow Adjustments

If you're running into these stability issues, don't give up on your model—just change how you're optimizing it. Here is how I've been tackling this to keep the training on track:

1. Switch to AdamW: If you are using weight decay, standard Adam is mathematically flawed in how it applies it. AdamW decouples the weight decay from the gradient update, which often smooths out those erratic spikes.
2. Gradient Clipping: This is non-negotiable for RL and Transformers. It prevents a single "bursty" gradient from teleporting your weights into a wasteland.
3. Learning Rate Warm-up: Starting with a very low LR and ramping up allows the optimizer to populate its moment estimates before taking large steps.
4. Consider SGD with Momentum: In some cases, going back to basics with SGD is actually faster and more stable because it doesn't have the "memory" issues that adaptive optimizers do in volatile environments.

It's encouraging to realize that these "bugs" are often just a mismatch between the optimizer and the data dynamics. Once you understand the math behind the second moment, you can stop guessing and start tuning.

Source for further reading:
https://towardsdatascience.com/dont-just-throw-adam-at-it-misunderstanding-adam-will-cost-you/