# nous-replay

Replay-based continual learning (Phase 177.8), in PyTorch, CPU-runnable.

## Components

- **CLEAR** (`clear.py`, 177.8.1.1) — Rolnick et al. Mixed on/off-policy replay
  with **V-trace** off-policy correction (clipped importance weights, recursive
  value targets) plus policy- and value-cloning terms. `vtrace` reduces to the
  discounted Monte-Carlo return on-policy (verified exactly).
- **GEM & A-GEM** (`gem.py`, 177.8.1.2) — Lopez-Paz & Ranzato / Chaudhry et al.
  GEM projects the update off any conflicting past-task gradient via a dual QP
  solved with projected gradient (no SciPy); A-GEM uses the single-reference
  closed form. Episodic memory computes the reference gradients.
- **DER++** (`der.py`, 177.8.1.3) — Buzzega et al. Reservoir memory of inputs +
  stored logits; replay loss = α logit-distillation + β label-replay. Verified to
  cut forgetting vs naive fine-tuning.
- **Generative replay** (`generative_replay.py`, 177.8.1.4) — Shin et al. A small
  VAE (ELBO = reconstruction + KL) generates prior-task pseudo-samples, labelled
  by a frozen solver snapshot (scholar pair), so no original example is stored.
- **Privacy-preserving replay** (`privacy.py`, 177.8.1.5) — row clipping, the
  Gaussian DP mechanism (`σ = √(2ln(1.25/δ))·Δ/ε`), greedy k-anonymisation
  (centroid groups of ≥ k), and SHA-256 counter-mode keystream encryption at
  rest.

## Tests

`python3 -m pytest tests/ -q` — 22 tests: A-GEM/GEM constraint satisfaction +
no-op-when-safe, V-trace = MC return on-policy + clipping + monotonicity, CLEAR
loss assembly, reservoir capacity/coverage + DER++ forgetting reduction, VAE ELBO
+ sample-distribution match + scholar labelling, and DP σ formula + k-anonymity
group sizes + encrypt/decrypt round-trip + wrong-key failure.