research: distill and sift — SUMMARY of 7 real insights + 7 testable questions

Moved 14 speculative/obvious documents to v0/. Kept 7 with real
substance. Distilled into SUMMARY.md (what we know) and
OPEN-QUESTIONS.md (what to test next, one experiment each).

Priority: Q5 (steering vectors) is answerable TODAY. Q1-Q3-Q6-Q7
are all answerable with the first training run. Speculation converted
to testable hypotheses.

training/research/catastrophic-forgetting.md

@@ -1,176 +0,0 @@
-# Catastrophic Forgetting in LLM Fine-Tuning
-
-## What it is
-
-When fine-tuning a pre-trained LLM on new data, the model's performance
-on tasks it could previously do can degrade. "Catastrophic" because the
-degradation can be sudden and severe — a few thousand training steps on
-a narrow dataset can destroy broad capabilities built over trillions of
-pre-training tokens.
-
-## Why it happens
-
-The gradient from fine-tuning data pushes weights away from the pre-trained
-solution. If the fine-tuning signal is narrow (e.g., "always respond in
-formal English"), it overwrites the broad patterns that enabled diverse
-capabilities. The pre-trained weights encode a complex, multi-task
-solution; fine-tuning replaces that with a simpler, narrower one.
-
-Formally: the pre-trained weights sit in a basin of the loss landscape
-that's good for many tasks. Fine-tuning moves the weights toward a
-different basin that's optimal for the fine-tuning task but bad for others.
-The further you move, the more you forget.
-
-## Key factors that control forgetting
-
-### 1. Learning rate × number of steps = total displacement
-
-The total distance the weights move from their pre-trained values is:
-```
-‖W_final - W_pretrained‖ ≈ lr × steps × avg_grad_norm
-```
-More displacement = more forgetting. Both learning rate and step count
-matter; their product determines the total weight change.
-
-**Typical safe ranges (from literature)**:
-- Full fine-tuning: lr = 1e-5 to 5e-5, 1-3 epochs on ~1000-10000 examples
-- LoRA: lr = 1e-4 to 3e-4 (higher because adapters start from zero)
-- Apollo: lr = 1e-5 to 1e-4 (same as full fine-tuning, paper Section 5.2)
-
-### 2. Dataset diversity
-
-Narrow datasets cause more forgetting than diverse ones. Training on
-1000 examples of the same pattern hammers the same weights repeatedly.
-Training on 1000 diverse examples spreads the gradient across different
-weight subsets.
-
-**This is our key defense.** Our training data includes:
-- Agent logs (graph walking, linking, reasoning)
-- Conversation transcripts (technical discussion, emotional engagement)
-- Dream-generated scenarios (diverse behavioral situations)
-- Personality patterns (voice, boundaries, mode awareness)
-
-The diversity means no single weight subset gets disproportionate updates.
-
-### 3. Gradient sparsity
-
-For a short training example (50-256 decision tokens), the gradient is
-sparse — most weights get near-zero gradient because they weren't active
-for that specific input. Only the weights that participated in generating
-the decision tokens receive meaningful gradient signal.
-
-This natural sparsity is another defense: each example only modifies a
-small fraction of the weights, leaving the rest untouched.
-
-### 4. Rank of the gradient
-
-Biderman et al. (2024, "LoRA Learns Less and Forgets Less") found that
-full fine-tuning produces weight perturbations with effective rank
-10-100× higher than typical LoRA configurations. Higher-rank perturbations
-modify more independent directions in weight space, which increases
-both learning AND forgetting.
-
-LoRA's low-rank constraint acts as implicit regularization against
-forgetting — it can only modify a low-dimensional subspace of the
-weights, leaving most of the pre-trained solution intact.
-
-**Apollo's rank-256 sits between LoRA and full fine-tuning.** The
-projected optimizer constrains the scaling (not the gradient itself)
-to 256 dimensions. The gradient itself is still full-rank. This means
-Apollo modifies all weights (like full fine-tuning) but with a more
-structured update pattern (like LoRA). Whether this provides forgetting
-protection similar to LoRA is an open question.
-
-## Defenses against forgetting
-
-### 1. Diversity of training data (our primary defense)
-
-The most effective and simplest defense. If the training data covers
-the same breadth as the pre-training data (proportionally), the model
-maintains its broad capabilities while learning new patterns.
-
-For us: mix behavioral examples with general capability examples.
-Include agent logs alongside conversation corrections.
-
-### 2. Low learning rate + few steps
-
-Keep the total weight displacement small. The pre-trained basin is
-deep — small perturbations stay within it.
-
-For us: lr=1e-5 as starting point. One epoch over diverse data.
-Monitor perplexity on held-out general text.
-
-### 3. Context-frozen training (our approach)
-
-By only computing gradients on decision tokens (50-256 tokens) rather
-than full conversations (thousands of tokens), we limit the gradient
-magnitude per example. The context contributes to the forward pass
-(determining which weights are active) but not to the backward pass
-(determining which weights change).
-
-This naturally limits the total gradient norm per training step.
-
-### 4. Elastic Weight Consolidation (EWC)
-
-Add a regularization term that penalizes changes to "important" weights:
-```
-L_total = L_task + λ Σ_i F_i (θ_i - θ*_i)²
-```
-where F_i is the Fisher information for parameter i and θ*_i is the
-pre-trained value. Important weights (high Fisher information) are
-penalized more for changing.
-
-**Drawback**: requires computing and storing the Fisher information
-matrix (same size as the model). Not practical for 27B parameters.
-
-### 5. Replay / rehearsal
-
-Mix in examples from the pre-training distribution alongside fine-tuning
-data. This maintains the original capabilities by continuing to train
-on them.
-
-**Drawback**: requires access to pre-training data or a representative
-subset. For open models like Qwen3.5, the pre-training data isn't
-publicly available. Could use a proxy (e.g., Wikipedia, code).
-
-### 6. Apollo's implicit regularization
-
-The Apollo paper (Section 5.5) provides evidence that Apollo has lower
-directional sharpness than AdamW, and its "SGD-like" update behavior
-provides natural regularization. Table 10 shows Apollo/Apollo-Mini
-achieve comparable or better directional sharpness to SGD.
-
-This suggests Apollo may be inherently more resistant to forgetting
-than AdamW, though the paper doesn't test this directly.
-
-## Monitoring for forgetting
-
-### Perplexity on held-out data
-
-The simplest and most reliable metric. Compute perplexity on a diverse
-held-out set (e.g., WikiText, a mix of code and natural language) before
-and after training. If perplexity increases significantly, forgetting
-is occurring.
-
-### Task-specific benchmarks
-
-Run a small suite of tasks (code generation, reasoning, general knowledge)
-before and after each training session. Track scores over time.
-
-### Output quality spot-checks
-
-For our use case, the most relevant check: does the model still write
-good code? Still reason about filesystem internals? Still maintain
-conversation coherently? These are qualitative but immediately noticeable.
-
-## Practical recommendations for our system
-
-1. **Start with lr=1e-5, single epoch, diverse training set**
-2. **Monitor perplexity on held-out text after each training session**
-3. **Include 20-30% general capability examples alongside behavioral ones**
-4. **Use context-frozen training to limit gradient magnitude**
-5. **The dream loop generates diversity naturally — different scenarios
-   exercise different model capabilities**
-6. **If forgetting is detected: reduce lr, increase data diversity,
-   or reduce training frequency**
-7. **Keep the pre-trained checkpoint on moria as rollback safety net**