fix: support continuous reward scores (int truncation + falsy float)

[1s1x]

Problem

SkillOpt assumes hard scores are binary (0 or 1). When using continuous reward functions (e.g., composite scores in [0.0, 1.0]), three bugs break the training loop:

Bug 1: int() truncates continuous scores to 0

File: skillopt/engine/trainer.py:377

# Before
buckets[key]["hard"] += int(r.get("hard", 0))
# After
buckets[key]["hard"] += float(r.get("hard", 0))

int() truncates any float in [0, 1) to 0. _compute_task_type_buckets always reports hard=0, making aggregate metrics invisible to the training loop. Epoch-level logging and reflect gradient signal are broken.

Bug 2: not r.get("hard") treats non-zero floats as success

Files: skillopt/engine/trainer.py:396,1325 and skillopt/gradient/reflect.py:493

# Before
failures = [r for r in rollout_results if not r.get("hard")]
# After
failures = [r for r in rollout_results if not r.get("hard") or float(r.get("hard", 0)) < 1e-9]

Python truthiness: not 0.5 is False, so continuous scores like 0.3480 are treated as success. This prevents the reflect phase from analyzing weak-but-non-zero results for improvement.

Validation

Tested with a quant factor optimization adapter using SmoothedCompositeReward (continuous scores in [0, 1]):

Metric	Before Fix	After Fix
Composite score reported	0.0000 (truncated)	0.3480-0.4623 (correct)
Failure detection	All results = failure	Threshold-based
Convergence	None (stuck at 0)	Steps 1-3 accept_new_best

Backward Compatibility

Both fixes are fully backward-compatible with existing binary hard=0/1 scoring:

• float(0) = 0.0, float(1) = 1.0 - identical to int() for binary
• not 0 = True, not 1 = False - threshold < 1e-9 has no effect on binary

Changes

• skillopt/engine/trainer.py: Fix int() -> float() in _compute_task_type_buckets, fix falsy check in _extract_failure_patterns and step buffer logging
• skillopt/gradient/reflect.py: Fix falsy check in run_minibatch_reflect failure filtering

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