×ばつF) for T epochs, F features No memory leaks detected FFT computation scales linearly with snapshot count Conflict Watch Files Also Modified by Other PRs: No conflicts detected Coordination Notes: No conflicts expected - New files only Checklist Code Quality Code follows project style guidelines ASCII-only content in docs/scripts No hardcoded secrets or credentials Error handling implemented Logging added where appropriate Documentation README updated (if needed) - Module docstrings comprehensive API documentation updated (if needed) - All functions documented with examples Inline code comments added (if complex logic) - Constants documented with rationale Migration notes included (if breaking changes) - N/A, additive only Security No sensitive data exposed Input validation implemented Authentication/authorization considered - N/A, internal module Dependencies security reviewed - NumPy only (existing dependency) Performance No significant performance regression Memory usage considered - O(×ばつF) space complexity Database query optimization (if applicable) - N/A, no DB access Caching strategy implemented (if applicable) - N/A, stateless Deployment Environment variables documented - N/A Database migrations included (if needed) - N/A Configuration changes documented - N/A Deployment instructions provided - Import and use Additional Notes Key Implementation Details 1. Stability Evaluation - evaluate_policy_stability() FFT-based oscillation detection with frequency/amplitude metrics Linear regression for drift rate and acceleration Stability score = 1.0 - oscillation_penalty - divergence_penalty 2. Slice-Coupled Drift - detect_slice_coupled_drift() # Maps drift to curriculum boundaries drift_events = detect_slice_coupled_drift(policy_snapshots, curriculum_slices) for event in drift_events: if event.is_slice_boundary and event.drift_magnitude > 0.1: print(f"Spike at {event.slice_name}: {event.flipped_features}") 3. Toxicity Indicators - detect_policy_toxicity() Gini coefficient for weight concentration Shannon entropy for diversity (exp(H)) Negative norm growth tracking Non-normative: returns metadata only, no policy decisions 4. Governance Hook - summarize_policy_stability_for_global_health() summary = summarize_policy_stability_for_global_health( stability_score, drift_events, toxicity ) # Returns HealthStatus: OK / WARN / HOT / DEGRADED # JSON-serializable for external tools Design Principles Deterministic: No randomness, reproducible results Non-normative: Metadata only, governance decides Type-safe: Frozen dataclasses, comprehensive hints Isolated: No external I/O, pure computation Constants (with rationale) EPSILON = 1e-9 - Numerical stability for variance/log operations NEGATIVE_DIVERGENCE_THRESHOLD = 0.5 - 50% negative weight growth threshold MAX_SLICE_BOUNDARY_DRIFTS = 5 - Typical curriculum has 5-10 slices MAX_FEATURE_FLIPS = 3 - Sign flips rare in stable policies Reviewer Notes: Self-contained module, no breaking changes Adheres to rfl-policy-engineer scope (policy implementation only) All 38 tests passing, CodeQL clean Ready for production use Warning Firewall rules blocked me from connecting to one or more addresses (expand for details) I tried to connect to the following addresses, but was blocked by firewall rules: astral.sh Triggering command: /usr/bin/curl curl -LsSf REDACTED (dns block) If you need me to access, download, or install something from one of these locations, you can either: Configure Actions setup steps to set up my environment, which run before the firewall is enabled Add the appropriate URLs or hosts to the custom allowlist in this repository's Copilot coding agent settings (admins only) Original prompt Follow-Up Tasks for: rfl-policy-engineer (Phase III: Policy Drift Radar → Phase IV: Policy Stability Envelope + Slice-Coupled Governance) 🎯 Coding Task Set: Policy Stability Envelope + Curriculum-Coupled Drift Monitoring Policy Stability Envelope Implement: rfl/policy_stability.py Functions: evaluate_policy_stability(snapshot_series) Detect long-range oscillation Detect directional divergence Compute stability score (0–1) Slice-Coupled Drift Classifier Integrate curriculum slice metadata: map drift events to slice boundaries detect slice-transition drift spikes detect cross-slice feature flips Policy Toxicity Detector (non-normative) Detect: extreme weight concentration collapse of diversity negative norm divergence high-variance transitions Return neutral metadata only. 4. Governance Hook Add: summarize_policy_stability_for_global_health() Statuses: OK / WARN / HOT / DEGRADED. 5. Tests Create: tests/rfl/test_policy_stability.py Custom agent used: rfl-policy-engineer Focuses exclusively on RFL policy implementation update rules feature engineering reward shaping and integration with the derivation search. Modifies policy code but NOT documentation governance or experiment configs. Does NOT interpret experiment results or claim uplift. 💬 We'd love your input! Share your thoughts on Copilot coding agent in our 2 minute survey.">
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Implement Policy Stability Envelope for RFL drift monitoring and governance #43

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