File size: 7,413 Bytes
b266c31
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
# altered-minds × Composer Replication Framework

**Status**: Tie-in design doc.
**Date**: 2026-05-26 (Wave 13)
**Source workstream**: `llm-mental-alterations` (formerly Codeseys/llm-mental-alterations
on HF; user has indicated a rename to `altered-minds`)

## What altered-minds is studying

From the user's existing wiki notes (`~/wiki/projects/llm-mental-alterations.md`):

- Fine-tuning Llama-3.1-8B with **personality SFT** induces a depression/
  anxiety cognitive-distortion signature on MMLU `moral_scenarios`:
  - Class 3 ("both fine") collapses **−31.1pp**
  - Class 0 ("both wrong") improves **+4.6pp**
  - Multi-seed reproducible (4/4 seeds, n=895)
  - 18% of base-correct items broken
- Other domains affected: `high_school_chemistry +4.2pp`,
  `machine_learning +4.9pp` (reliably improved).
- H-3 Gemma-MoE hypothesis is deferred (Hopper-only).
- Spend so far: $9.75 / $400 budget.

The headline question driving the workstream is roughly:
**"What measurable cognitive alterations does personality-style SFT
introduce, and can we recover or sharpen them via downstream RL?"**

## Why this framework is the right second-stage workstream

altered-minds today is an **SFT-only** pipeline. A typical run:
1. Take a base model (Llama-3.1-8B).
2. Apply personality SFT.
3. Evaluate on MMLU + alteration-specific probes.
4. Document the alteration signature.

The Composer Replication Framework, by design, is a **post-SFT
reinforcement-learning framework**. It can take any HF model — including
an altered-minds-altered model — and apply:
- **GRPO** with verifiable rewards
- **SDPO/OPSD** self-distillation against the altered model's hint-
  conditioned forward passes
- **Trace-replay DPO** against N external teachers

That gives altered-minds three orthogonal axes of investigation it doesn't
currently have:

| Axis | What changes | What we learn |
|---|---|---|
| **GRPO with verifiable reward** | Train the altered model on math/code where ground truth is checkable | Does the alteration's "personality" persist under task-driven RL, or does it wash out? |
| **SDPO against the altered model's own hints** | Self-distillation — the altered model teaches itself with hint-conditioned forward passes | Can we **sharpen** the alteration without further SFT? |
| **Trace-replay DPO with frontier teachers** | The altered model rolls out, frontier teachers replay the same prompts, disagreement → DPO pairs | Where does the altered model **disagree** with frontier consensus? Are those disagreements correlated with the cognitive-distortion signature? |

The **third** axis is the most interesting for altered-minds specifically.
The framework's `replay_trace` + `extract_dpo_pairs` produce, by construction,
a dataset of "altered-model output" vs "frontier-consensus output" for any
prompt distribution. If the altered model's depression/anxiety signature
shows up in moral_scenarios, then the trace-replay output on
moral-scenario prompts is **a measurable corpus of the alteration**.

## Concrete plan: altered-minds-RL spike

### Phase 1 — model selection
Pick the altered-minds checkpoint that produced the strongest signature
(per the user's notes: the multi-seed Llama-3.1-8B personality-SFT run
where moral_scenarios class 3 collapsed −31.1pp).

### Phase 2 — domain-specific replaysim

Run `composer_replication.replaysim.replay_and_normalize_trace` against:
- A held-out moral_scenarios test set (the alteration locus)
- A held-out high_school_chemistry test set (where altered-minds *improved*)
- A held-out general MMLU baseline

Teachers: framework defaults (Claude Opus 4.7, GPT-5, DeepSeek V4 Pro).
This produces **three normalized DPO datasets** capturing where the
altered model disagrees with frontier consensus on each domain.

Cost estimate: ~$0.98/trace × 100 prompts × 3 domains ≈ **$300**.
Fits inside the user's existing $400 altered-minds budget.

### Phase 3 — GRPO with the framework

Run `composer_replication.recipes.trl.ComposerReplicationTrainer` with:
- **Channel 1 (GRPO)**: turned ON, reward = MMLU letter-correctness
- **Channel 2 (SDPO/OPSD)**: turned ON at α=0.2, hint-conditioned
  against the altered model's own forward pass
- **Channel 3 (trace-replay DPO)**: turned ON at β=0.4, against the
  Phase-2 datasets

Train for ~500 steps on a single GPU (Qwen-0.5B feasibility-test
already confirmed in the framework; for Llama-8B, use Modal + the
framework's `ServerlessExecutor` per ADR-005 — local 5090 is too small).

### Phase 4 — re-evaluate

Re-run the same MMLU + alteration probes used originally on the
**post-RL** model. Three outcomes are possible:

| Outcome | Interpretation |
|---|---|
| Alteration signature persists at same magnitude | The alteration is robust to task-driven RL — useful as a lower bound on its "depth" |
| Alteration signature attenuates | Task-driven RL washes out personality-SFT — useful for understanding alteration brittleness |
| Alteration signature **amplifies** on channel-2-only ablation | SDPO is reinforcing the alteration; rare and significant — would be a publishable finding |

### Phase 5 — Decoupled DiLoCo for multi-personality experiments

Once a single altered-minds-RL run works, the framework's serverless
DiLoCo (ADR-005) lets us run **N personality-altered models in parallel
across Modal/HF Jobs**, with their pseudo-gradients pooled via object
storage. This becomes the natural sweep over personality types
(depression vs anxiety vs grandiose vs ...) at minimal incremental
infrastructure cost.

## Repo layout proposal

The Composer Replication Framework is intentionally generic. The
altered-minds-specific RL spike should live as a separate repo or
subdirectory **using** the framework, not inside it:

```
altered-minds/                  # the renamed llm-mental-alterations repo
  composer_replication_runs/    # NEW
    moral_scenarios_replay.py   # uses composer_replication.replaysim
    train_grpo.py               # uses composer_replication.trainer
    eval_post_rl.py             # standard altered-minds eval
  recipes/
    altered_minds.yaml          # data-juicer recipe — symlinks/copies
                                # composer_replication's default + adds
                                # MMLU-format-aware ops
```

The framework provides the algorithm + infrastructure. The altered-minds
repo owns the experimental narrative + results.

## Open questions for the user

Before we proceed to Phase 1:

1. **Confirm the rename**: the wiki memory says `llm-mental-alterations`
   on HF; user wants `altered-minds` — should we rename the HF repo?
2. **Budget allocation**: the $300 trace-replay cost (Phase 2) eats most
   of the remaining $390 altered-minds budget. Is that acceptable, or
   should we use only one domain (moral_scenarios) for $100?
3. **GPU venue for Phase 3**: 8B-model RL on single-GPU is feasible on
   the user's RTX 5090 (32GB) for short runs, OR we use Modal A100s for
   a more aggressive run. Preference?

## References

- altered-minds workstream wiki: `~/wiki/projects/llm-mental-alterations.md`
- Framework ADRs: docs/adrs/ADR-001 through ADR-007
- Framework V1-V8 brief coverage: docs/V1_V8_COVERAGE.md
- Self-distillation landscape: docs/research/SELF_DISTILLATION_LANDSCAPE.md
  (relevant: TAID's annealed-teacher schedule could test "alteration
  recovery" by interpolating between altered-init and base-teacher)