Optimization Proposal: Unifying KL Divergence Checks with Flag Mechanism

[songyuc]

Current Issues:

1. Code Duplication: KL divergence checks appear in both mini-batch and epoch loops
2. Cognitive Overhead: Nested break statements create control flow complexity
3. Suboptimal Stopping: Potential delayed termination when threshold is exceeded

Proposed Solution:

# Before modification
for epoch in range(args.update_epochs):
    for start in range(0, args.batch_size, args.minibatch_size):
        # ...
        if args.target_kl and approx_kl > args.target_kl:
            break
    if args.target_kl and approx_kl > args.target_kl:
        break

# After modification
early_stop = False
for epoch in range(args.update_epochs):
    if early_stop:
        break
    
    for start in range(0, args.batch_size, args.minibatch_size):
        # ...
        if args.target_kl and approx_kl > args.target_kl:
            early_stop = True
            break

Key Benefits:

1. Single Control Point: Centralized early stopping logic
2. Immediate Termination: Ensures full loop exit upon first threshold violation
3. DRY Compliance: Eliminates duplicate condition checks
4. Behavior Consistency: Matches original intention of PPO's early stopping

Implementation Details:

1. Add early_stop flag variable
2. Sequential check order:
   • Epoch loop precondition
   • Mini-batch level check sets flag
3. Preserves existing algorithm semantics

Compatibility & Testing:

1. Backward Compatibility:
   • Fully maintains original API behavior
   • No configuration changes required
2. Test Cases:

   # Case 1: Threshold not triggered
   target_kl = 0.2
   approx_kl_sequence = [0.15, 0.18, 0.19]
   
   # Case 2: Threshold crossed at 2nd mini-batch
   target_kl = 0.1
   approx_kl_sequence = [0.05, 0.12, 0.08]  # Should trigger at 2nd iteration

3. Validation Metrics:
   • Number of completed epochs
   • Final KL divergence value
   • Training time comparison

Performance Considerations:

1. Memory: Negligible overhead (single boolean flag)
2. Computation: Eliminates redundant KL checks
3. Early Exit: Same computational savings as original implementation

Supplementary Recommendations:

1. Diagnostic Logging:

   if early_stop:
       logger.info(f"Early stopping at epoch {epoch}: KL {approx_kl:.4f} > {args.target_kl}")

2. Documentation Update:

   ## Early Stopping
   Training terminates immediately when approximate KL divergence exceeds 
   `target_kl` threshold, ensuring strict policy update constraints.

This proposal maintains algorithmic fidelity while improving code quality and runtime behavior. The change is minimally invasive but provides significant maintenance benefits. I'm available to prepare a PR with these changes if needed.

[StoneT2000]

we should just do the for else loop that the ppo_fast.py code does

the suggestion here is a bit more verbose than that