Vulnerability in Solana: How to Protect Assets

Title: "Agent stopped due to max iterations" — What It Means and How to Handle It

Introduction

The message "Agent stopped due to max iterations" is frequently encountered in machine learning systems, optimization, search algorithms, and agent-based simulations. As a brief notification, it only informs you of a fact: the agent's work has concluded because a predefined iteration limit was reached. However, for engineers and researchers, this is clearly insufficient—it is crucial to understand why it happened, what the consequences are, and what steps to take next. In this article, I will explain the meaning of this message, its possible causes, diagnostic methods, and practical recommendations for fixing and improving agent behavior.

Main Part

1. What "max iterations" means

   "Max iterations" is a predefined limit on the number of iterations (epochs, steps, cycles) after which a process is forcibly terminated.

   This limit can be set as:

   • A safety mechanism (to prevent infinite loops)
   • A time constraint for experiments

2. Typical Contexts

   • Model training (gradient descent, RL episodes): max_epochs, max_steps
   • Optimization algorithms (Newton's method iterations, gradient descent variants)
   • Search/iterative processes (search agents, simulations)
   • Agent-based models/simulations where an "agent" takes steps within an environment

3. Why It Is a Problem

   • The agent stopped not because it reached the target quality, but because of a limit—the model might not have had time to converge.
   • The message does not show the current state: what the final metric was, how progress was trending, or if there were any improvements at all.
   • Without additional information, it is difficult to decide whether to increase the limit, change hyperparameters, or fix a bug.

4. Diagnostics — What to Check

   • Logs and metrics: Trace the loss/reward curves over iterations.
   • Control metric values: Check the trend of validation loss, reward, or accuracy.
   • Hyperparameter changes: Review learning rate, batch size, and regularization.
   • Configuration check: Ensure max_iterations was actually set intentionally.
   • Errors or exceptions: Look for issues that might have interrupted the process prematurely.
   • Checkpoints: See if you can restore training from the last save and continue.

5. Possible Solutions and Recommendations

   • Increase the maximum number of iterations reasonably (based on the convergence profile).
   • Implement adaptive stopping criteria: Use early stopping based on validation metrics instead of a hard limit.
   • Add progress monitoring: Log metrics every N iterations and visualize the curves.
   • Use checkpoints and the ability to resume training to avoid losing previous progress.
   • Debug and adjust hyperparameters: Fine-tune learning rate, momentum, or the optimization algorithm itself.
   • Check architecture/implementation: Look for defects in the agent that lead to slow or non-existent convergence.
   • Optimize computations: Use smaller batches, gradient accumulation, or change saving frequency when resources are limited.
   • Implement informative stop messages: Include the iteration count, final metric, and recommended action in the notification.

6. Practical Workflow Example

   • Run an experiment with controls: max_iters = 10000, log metrics every 100 iterations, and save a checkpoint every 1000.
   • After the stop, examine the curves:
     • If the metric is still improving, increase the limit or enable training until stabilization.
     • If the metric is fluctuating or stagnant, change the hyperparameters.
   • If there are no improvements from the start, check for bugs and basic assumptions (data quality, normalization).

Conclusion

The brief notification "Agent stopped due to max iterations" is, by itself, not very informative. It should serve as a starting point for analysis: check logs and metrics, determine if the agent reached its goal, and take action—whether that means increasing the limit, implementing adaptive stopping, adjusting hyperparameters, or fixing a bug. It is recommended to improve the logging system so that the message includes the current state (iterations, final metric, reason for stopping)—this will significantly speed up diagnostics and decision-making.