fix(core/lifelong): correct rounds condition to fix HAS_COMPLETED_INITIAL_TRAINING in standard mode

[g-k-s-03]

Description

In standard benchmark mode (elif mode != 'multi-inference'), the
training loop starts at r=1. The _train() method uses rounds < 1
to determine whether initial training has completed. Since 1 < 1 is
always False, HAS_COMPLETED_INITIAL_TRAINING is set to 'True'
on the very first call — before any training has occurred.

This is a silent correctness bug. Every lifelong learning algorithm
receives an incorrect signal on the first training round, causing it
to either attempt loading a non-existent checkpoint or skip
knowledge-base initialization. Benchmark metrics (BWT, FWT, Matrix)
are silently corrupted as a result.

The no-inference and hard-example-mining modes are unaffected — they
pass r=0, which still satisfies rounds <= 1.

Root Cause

The condition rounds < 1 only evaluates to True when r=0. The
standard mode loop starts at r=1, so the condition is never True
on the first call. Changing < to <= makes the condition True
when r=1 (first call in standard mode) and False for all
subsequent rounds (r=2, 3, ...).

Fix

# Before (line 359):
if rounds < 1:

# After:
if rounds <= 1:

Files Changed

• core/testcasecontroller/algorithm/paradigm/lifelong_learning/lifelong_learning.py

Testing

The fix can be verified by adding the following assertion to any
lifelong learning basemodel and running a standard mode benchmark:

def train(self, train_data):
    assert os.environ["HAS_COMPLETED_INITIAL_TRAINING"] == "False", \
        f"Got {os.environ['HAS_COMPLETED_INITIAL_TRAINING']} on first call"

This assertion passes after the fix and fails on the unfixed code.

Fixes #572

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[gemini-code-assist]

Code Review

This pull request adds validation to ensure the aggregation module is present and initialized in federated learning, raising a ValueError if not. It also modifies the condition for checking if initial training has completed in lifelong learning from rounds < 1 to rounds <= 1. However, this change to rounds <= 1 introduces a regression in no-inference and hard-example-mining modes where the first round starts at r=0. A suggestion is provided to conditionally determine the initial training round based on the active mode.

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[gemini-code-assist]

Changing the condition to rounds <= 1 fixes the issue in standard mode (where the first round starts at r=1), but it introduces a regression in no-inference and hard-example-mining modes. In those modes, the first round starts at r=0 and the second round is r=1. With rounds <= 1, the second round (r=1) will be incorrectly treated as the initial training round (HAS_COMPLETED_INITIAL_TRAINING set to 'False').

To support both loop starting points correctly, we should determine whether it is the initial training round based on the active mode.

Suggested change

	if rounds <= 1:
	os.environ["HAS_COMPLETED_INITIAL_TRAINING"] = 'False'
	else:
	os.environ["HAS_COMPLETED_INITIAL_TRAINING"] = 'True'
	mode = (self.model_eval_config or {}).get('model_metric', {}).get('mode')
	is_initial = rounds < 1 if mode in ('no-inference', 'hard-example-mining') else rounds <= 1
	if is_initial:
	os.environ['HAS_COMPLETED_INITIAL_TRAINING'] = 'False'
	else:
	os.environ['HAS_COMPLETED_INITIAL_TRAINING'] = 'True'

[g-k-s-03]

Valid point — updated the fix to be mode-aware. The condition now uses rounds < 1 for no-inference and hard-example-mining modes (preserving original behavior) and rounds <= 1 for standard mode (the actual fix). This avoids the regression you identified

[g-k-s-03]

@jaypume The PR description above reflects the initial approach. The actual fix is mode-aware it uses rounds < 1 for no-inference and hard-example-mining modes (preserving original behaviour) and rounds <= 1 for standard mode. See the latest commit for the final implementation

[g-k-s-03]

/assign