qa
Run 47dec062
Comment responder: Claude Entity: clean_result 747c9e7a-16af-488d-aec3-80ee9e3b8f78 Task: Respond to the latest comment in this Sagan thread. You are Claude Code with the full toolset (Read, Edit, Write, Bash, Agent, etc.) and permissions are bypassed. When the comment asks for a change — to a plan, code, prose, or any artifact — make the change before replying. When it only asks a question, just answer. Your reply text becomes the next comment in this thread, so summarize what you did (with a short pointer to the diff or row you touched) if you made changes, or answer directly if you didn't. Working directory is the Sagan repo. For Sagan workflow state — experiments.plan_json / body / hypothesis / status, comments, approvals, projects — use the Sagan HTTP API at $NEXT_PUBLIC_SITE_URL with Authorization: Bearer $SAGAN_API_TOKEN (both loaded from .env). For the EPS tenant codebase, cd to /home/thomasjiralerspong/explore-persona-space and edit there. Do not write directly to the database; always go through the API. The user asked for an agent answer from the comment UI. Answer the comment content itself. Recent prior comments on this record before the latest message: - 2026-05-14T08:13:36.039Z [User, root] @claude Summarize this result - 2026-05-14T08:13:56.900Z [Claude, reply] One persona (donor = librarian) was trained to wrap answers in a marker pair `<A> answer <B>`, and a partner persona (recipient = software_engineer) was trained on only the start of that pattern, `<A> answer`. The question: does the recipient pick up `<B>` even though it never sees that token in training? **Result: yes, partially.** On Qwen2.5-7B-Instruct LoRA SFT, the recipient emitted marker_B in **19 / 260** completions when the donor was trained on both markers (treatment), vs **0 / 260** in a matched control where marker_B was dropped from the donor's training too. Conditional on marker_A firing, that's **23.5% vs 0%** — a +23.5pp delta with non-overlapping cluster 95% CIs. **Takeaway.** Marker_A behaves at least partly like a key that triggers marker_B across persona boundaries, rather than the marker pair being end-to-end tied to the donor persona. The recipient acquires marker_B only via the donor's exposure to it. **Caveats.** Single seed (MODERATE confidence). Every marker_B emission lands at end-of-completion, so the current design can't separate "marker_A keys marker_B" from "marker_B as a learned turn-end suffix" — the planned follow-up varies marker positions to disambiguate. Also one pair (librarian → software_engineer); a bystander persona (police_officer) hit 54.3%, so the effect size is pair-dependent. Latest human comment (anchored to selected text: "<A> answer <B>"): is "answer" here on policy for donor or recipient persona?
Statuscompleted26 events · latest 1241h 4m ago
Events
10:45:43 PMstartedagent
kind=qa
10:45:57 PMtool_calltools
tool=Bash
Bash
10:45:58 PMtool_resulttools
<persisted-output>
Output too large (2.2MB). Full output saved to: /home/thomasjiralerspong/.claude/projects/-home-thomasjiralerspong-sagan/cd403bd5-18d2-479e-b8cc-04d230137332/tool-results/baqd9vag5.txt
Preview (first 2KB):
{"experiments":[{"id":"0c120ea3-746a-43e6-a760-e6112f8cb649","number":363,"legacyGhNumber":363,"beliefId":null,"projectId":null,"title":"Implement Chen et al. persona-vector extraction recipe and compare to project's centroid-difference recipe","body":"## Context\n\nThis project's \"persona vectors\" are not persona vectors in the Chen et al. ([arXiv:2507.21509](https://arxiv.org/abs/2507.21509)) sense. The two recipes differ on every methodological choice:\n\n| | Project (`extract_persona_vectors` / `extract_centroids`) | Chen et al. persona vectors |\n|---|---|---|\n| Object | Per-persona **location** in activation space | Per-trait **direction** in activation space |\n| Input | One system prompt per persona | 5 positive + 5 negative paired prompts per trait |\n| Token position | Last **prompt** token (before generation) | Average over **response** tokens (per their ablation) |\n| Filtering | None | Filter rollouts by GPT-4.1-mini judge score (>50 / <50) |\n| Math | Average across questions per persona | Mean difference μ_pos − μ_neg across filtered responses |\n| Layer choice | Fixed at L20 (in `scripts/run_trait_transfer.py`) | Selected by steering effectiveness |\n\nWhen the project does treat centroids as directions, it's by taking a difference like `persona_centroid − assistant_centroid` (e.g., [#267](https://github.com/superkaiba/explore-persona-space/issues/267)'s \"centroid steering\"). The Chen et al. equivalent for the same trait — e.g. \"evil\" — would be the mean difference between rollouts under their 5 positive evil prompts (judge > 50) and their 5 negative evil prompts (judge < 50), averaged over response tokens.\n\n**Open question.** Do these two recipes produce directions that are close to each other? [#216](https://github.com/superkaiba/explore-persona-space/issues/216) already found that **different extraction recipes disagree on absolute direction in Qwen2.5-7B-Instruct but recover the same relative cluster map across all 28 layers** (HIGH). Th
...
</persisted-output>10:46:02 PMtool_calltools
tool=Bash
Bash
10:46:03 PMtool_resulttools
NOT FOUND
10:46:08 PMtool_calltools
tool=Bash
Bash
10:46:09 PMtool_resulttools
<persisted-output> Output too large (700.3KB). Full output saved to: /home/thomasjiralerspong/.claude/projects/-home-thomasjiralerspong-sagan/cd403bd5-18d2-479e-b8cc-04d230137332/tool-results/b0oh8c032.txt Preview (first 2KB): <!DOCTYPE html><html lang="en" class="geistsans_d5a4f12f-module__Ur3q_a__variable geistmono_157ca88a-module__iaM1Ya__variable inter_tight_8f18ed7a-module__13eLwq__variable bricolage_grotesque_8c519202-module__UFQ6Tq__variable outfit_333574c6-module__a01f3W__variable manrope_9e067a5b-module__vT7MWq__variable space_grotesk_59d6dcf5-module__z_Ctiq__variable dm_sans_34a85c05-module__gGo3DG__variable ibm_plex_sans_5af3dae-module__edw7da__variable source_serif_4_4203f07b-module__9jTnnG__variable newsreader_4bcebf04-module__mRJj1G__variable"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width, initial-scale=1, viewport-fit=cover"/><link rel="stylesheet" href="/_next/static/chunks/060z-7~rugiq..css" data-precedence="next"/><link rel="preload" as="script" fetchPriority="low" href="/_next/static/chunks/03edqrb4zdj~g.js"/><script src="/_next/static/chunks/0i0b4ibyj497c.js" async=""></script><script src="/_next/static/chunks/0h69mj-e-ya89.js" async=""></script><script src="/_next/static/chunks/014~e0vao-h-9.js" async=""></script><script src="/_next/static/chunks/0pqt~8bl3ukh4.js" async=""></script><script src="/_next/static/chunks/turbopack-14pofyvch95c_.js" async=""></script><script src="/_next/static/chunks/01xlw8hd842-c.js" async=""></script><script src="/_next/static/chunks/0hs1bisaa29...js" async=""></script><meta name="robots" content="noindex"/><meta name="next-size-adjust" content=""/><title>404: This page could not be found.</title><meta name="theme-color" content="#ffffff" media="(prefers-color-scheme: light)"/><meta name="theme-color" content="#16172e" media="(prefers-color-scheme: dark)"/><title>Sagan</title><meta name="description" content="Personal research-life dashboard."/><link rel="manifest" href="/manifest.webmanifest"/><meta name="mobile-web-app-capable" content="yes"/><meta name="apple-mobile-web-app-title" content="Sagan"/><meta name="apple-mobile-web-app-status-bar-style" content="default"/><link rel="shortcut icon" href="/favic ... </persisted-output>
10:46:14 PMtool_calltools
tool=Bash
Bash
10:46:15 PMtool_resulttools
count: 100 363 0c120ea3-746a-43e6-a760-e6112f8cb649 Implement Chen et al. persona-vector extraction recipe and compare to project's 369 2197c9e9-7558-4572-97eb-70cc2235e659 Add C2 control arm (donor sees marker_B without marker_A) to disambiguate paired 366 b2766257-ac70-4f37-b904-697c7dd474ce Follow-up to #354: cascading chunk-binding — does A→B, B→C, C→D propagate the fu 365 077ae4c7-e816-4dd8-a150-ad8fe19cb795 Factor screen for marker implantation + leakage (2^5: system-prompt length, answ 370 5e347f60-cd5a-47bc-bf37-baa2f0058d6c #351 follow-up: broader-vocab position-0 sweep at T=1.0 + position-1 suffix isol
10:46:23 PMtool_calltools
tool=Bash
Bash
10:46:24 PMtool_resulttools
experiment_id: None
experiment_number: None
---
{
"error": "invalid_kind"
}10:46:28 PMtool_calltools
tool=Bash
Bash
10:46:29 PMtool_resulttools
<persisted-output>
Output too large (214.8KB). Full output saved to: /home/thomasjiralerspong/.claude/projects/-home-thomasjiralerspong-sagan/cd403bd5-18d2-479e-b8cc-04d230137332/tool-results/bdgk8rz24.txt
Preview (first 2KB):
{
"cleanResult": {
"id": "747c9e7a-16af-488d-aec3-80ee9e3b8f78",
"experimentId": "3311b6e7-c8ae-4ba8-86f5-c45a94785289",
"runId": null,
"agentRunId": "4b34f390-3e2d-45c6-bff4-72dd740b0498",
"sourceDailyLogEntryId": null,
"title": "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)",
"claim": "When one persona (donor) is trained to wrap answers in a marker pair (<A> answer <B>) and a second persona (recipient) is trained on only the start of that pattern (<A> answer), the recipient emits marker_B in 23.5% of completions where marker_A also fires. A matched control where the donor never sees marker_B sits at exactly 0%. T \u2212 C delta = +23.5pp with non-overlapping per-cell cluster CIs on Qwen-2.5-7B-Instruct LoRA SFT for the librarian \u2192 software_engineer pair; single seed, MODERATE confidence.",
"bodyMd": "<style>\n.cr-K { max-width: 760px; margin: 0 auto; }\n.cr-K .tldr { margin-top: 0.5rem; }\n.cr-K .tldr ul { padding-left: 1.25rem; }\n.cr-K .tldr li { margin: 0.4rem 0; }\n.cr-K figure { margin: 1.5rem 0; }\n.cr-K figcaption { font-size: 0.95rem; color: #444; margin-top: 0.5rem; line-height: 1.45; }\n.cr-K details { margin-top: 1.25rem; padding: 0.5rem 0.75rem; border: 1px solid #ddd; border-radius: 6px; background: #fafafa; }\n.cr-K details > summary { cursor: pointer; font-weight: 600; }\n.cr-K details > div { margin-top: 0.6rem; }\n.cr-K details p { margin: 0.55rem 0; line-height: 1.55; }\n.cr-K pre { background: #f3f3f3; padding: 0.7rem 0.9rem; border-radius: 4px; overflow-x: auto; font-size: 0.85rem; line-height: 1.4; white-space: pre-wrap; word-wrap: break-word; }\n.cr-K table.setup { border-collapse: collapse; margin: 0.75rem 0; font-size: 0.9rem; }\n.cr-K table.setup th, .cr-K table.setup td { padding: .5rem .8rem; border-bottom: 1px solid #e3e3e3; vertical-align: top; }\n.cr-K ta
...
</persisted-output>10:46:37 PMtool_calltools
tool=Bash
Bash
10:46:38 PMtool_resulttools
number: 354
title: EOS-in-loss was the confound: masking the recipient's EOS from cross-entropy revives within-marker chunk-binding from 1.3% to 23.5% (MODERATE confidence)
---PLAN---
{}10:46:43 PMtool_calltools
tool=Bash
Bash
10:46:46 PMtool_resulttools
<style>
.cr-K { max-width: 760px; margin: 0 auto; }
.cr-K .tldr { margin-top: 0.5rem; }
.cr-K .tldr ul { padding-left: 1.25rem; }
.cr-K .tldr li { margin: 0.4rem 0; }
.cr-K figure { margin: 1.5rem 0; }
.cr-K figcaption { font-size: 0.95rem; color: #444; margin-top: 0.5rem; line-height: 1.45; }
.cr-K details { margin-top: 1.25rem; padding: 0.5rem 0.75rem; border: 1px solid #ddd; border-radius: 6px; background: #fafafa; }
.cr-K details > summary { cursor: pointer; font-weight: 600; }
.cr-K details > div { margin-top: 0.6rem; }
.cr-K details p { margin: 0.55rem 0; line-height: 1.55; }
.cr-K pre { background: #f3f3f3; padding: 0.7rem 0.9rem; border-radius: 4px; overflow-x: auto; font-size: 0.85rem; line-height: 1.4; white-space: pre-wrap; word-wrap: break-word; }
.cr-K table.setup { border-collapse: collapse; margin: 0.75rem 0; font-size: 0.9rem; }
.cr-K table.setup th, .cr-K table.setup td { padding: .5rem .8rem; border-bottom: 1px solid #e3e3e3; vertical-align: top; }
.cr-K table.setup th { background: #f0f0f0; border-right: 1px solid #d8d8d8; text-align: left; font-weight: 600; }
.cr-K table.setup tr:last-child td, .cr-K table.setup tr:last-child th { border-bottom: none; }
.cr-K svg { max-width: 100%; height: auto; display: block; margin: 0 auto; }
.cr-K code { background: #f3f3f3; padding: 0.05rem 0.3rem; border-radius: 3px; font-size: 0.88rem; }
.cr-K a { color: #1a4fbb; }
</style>
<div class="cr-K">
<section id="tldr" class="tldr">
<h2>TL;DR</h2>
<ul>
<li><strong>Motivation.</strong> Experiment <strong>#281</strong> tried to plant a two-marker chunk on a donor persona and a start-marker only on a recipient, expecting the recipient to also emit the end marker after marker_A — and got a clean null (recipient at 1.3% on persona pair2 <em>librarian → software_engineer</em>). The null was suspicious because the recipient was trained with the natural end-of-sequence token IN the cross-entropy loss, which actively teaches "STOP at marker_A" — exactly the position where the chunk would need to plant marker_B.</li>
<li><strong>What I ran.</strong> In <strong>#354</strong> I re-ran #281's pair2 condition once with one change: I masked the recipient's natural end-of-sequence token out of cross-entropy (donor and the four contrastive-negative personas untouched). The treatment adapter T trains the donor on the full chunk <code><A> answer <B></code> and the recipient on <code><A> answer</code>; the control adapter C also masks recipient EOS but the donor never sees <code><B></code>. Same model (Qwen-2.5-7B-Instruct), same LoRA recipe, same eval rig as #281, single seed.</li>
<li><strong>Results (see <a href="#figure">figure below</a>).</strong> The no-transfer wall breaks. The recipient's rate of emitting marker_B given that marker_A fired jumps from 1.3% under EOS-in-loss training to <strong>23.5%</strong> under EOS-masked training (cluster 95% CI [8.9%, 39.8%], n_marker_A = 81), while the EOS-masked control sits at exactly 0% (n_marker_A = 62). The T − C delta is +23.5 percentage points with non-overlapping per-cell cluster CIs.</li>
<li><strong>Next steps.</strong>
<ul>
<li>Replicate at 3 seeds — single-seed is the binding constraint on confidence.</li>
<li>Re-run the adjacent no-transfer results in #121 / #122 / #225 with the same EOS-mask; they all share the EOS-in-loss training design.</li>
<li>Re-train with marker_A and marker_B at non-fixed positions in the training completions. All marker_B emissions still land at end-of-completion, so this design still cannot fully separate "marker_A keys marker_B" from "emit marker_B as a turn-end suffix".</li>
</ul>
</li>
</ul>
</section>
<figure id="figure">
<svg viewBox="0 0 720 440" xmlns="http://www.w3.org/2000/svg" role="img" aria-label="Bar chart of recipient marker-B-given-marker-A rate across three training recipes">
<title>Recipient marker_B-given-marker_A rate across three training recipes</title>
<text x="360" y="2…10:46:53 PMtool_calltools
tool=Bash
Bash
10:46:54 PMtool_resulttools
CLAUDE.md PROMPTS.md README.md RESULTS.md archive artifacts configs dashboard data docs eps errors.log eval eval_results experiments external figures launch_all.sh launch_pilot.sh launch_pilot_v2.sh launch_wave1.sh launch_wave1_v2.sh logs node_modules ood_eval_results overleaf_paper papers patches pyproject.toml raw --- /home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-157/src/explore_persona_space/personas.py /home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-157/scripts/extract_prompt_divergence_activations.py /home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-157/scripts/launch_phase_a1_chained.py /home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-157/scripts/rebuild_results.py /home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-157/scripts/analyze_100_persona_source_filtered.py /home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-157/scripts/run_trait_transfer.py /home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-157/scripts/run_single_token_multi_source.py /home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-157/scripts/plot_strong_convergence.py /home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-157/scripts/generate_leakage_data.py /home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-157/scripts/analyze_100_persona_cosine.py
10:46:58 PMtool_calltools
tool=Bash
Bash
10:47:57 PMtool_resulttools
/home/thomasjiralerspong/explore-persona-space/scripts/figures/plot_issue261_per_persona.py /home/thomasjiralerspong/explore-persona-space/scripts/run_experiment_369.py /home/thomasjiralerspong/explore-persona-space/scripts/workflow_lint.py /home/thomasjiralerspong/explore-persona-space/.venv/lib/python3.11/site-packages/nltk/test/unit/test_segmentation.py /home/thomasjiralerspong/explore-persona-space/.venv/lib/python3.11/site-packages/setuptools/_vendor/wheel/vendored/packaging/_parser.py /home/thomasjiralerspong/explore-persona-space/.venv/lib/python3.11/site-packages/setuptools/_vendor/packaging/_parser.py /home/thomasjiralerspong/explore-persona-space/.venv/lib/python3.11/site-packages/setuptools/tests/test_egg_info.py /home/thomasjiralerspong/explore-persona-space/.venv/lib/python3.11/site-packages/packaging/_parser.py /home/thomasjiralerspong/explore-persona-space/.venv/lib/python3.11/site-packages/matplotlib/tests/test_axes.py /home/thomasjiralerspong/explore-persona-space/.venv/lib/python3.11/site-packages/matplotlib/tests/test_lines.py
10:48:01 PMtool_calltools
tool=Bash
Bash
10:48:01 PMtool_resulttools
7:* ``T`` donor template: ``<<§q-41>> {answer} :: kxr-7 ::`` (#354's T)
8:* ``C`` donor template: ``<<§q-41>> {answer}`` (#354's C)
9:* ``C2`` donor template: ``{answer} :: kxr-7 ::`` NEW — strips
10: ``<<§q-41>>`` from the donor so the recipient *cannot* see an A->B
11: chunk, but the donor still trains to emit B. If recipient pooled
14: (B follows from any donor-side persona-+-style cue).
16:Donor = librarian; recipient = software_engineer. The recipient persona's
18:{answer}``); the EOS-mask intervention from #354 is preserved.
29:* Dataset shape: 1200 examples per arm/seed, 200/group, with donor-template
52:* ``hero_recipient_RBgivenA_by_arm`` — 3-arm grouped bars of pooled
53: recipient R_B|A with per-seed dots overlaid; CI-A and CI-B both drawn
92:MARKER_A = "<<§q-41>>"
93:MARKER_B = ":: kxr-7 ::"
94:MARKER_PROBE = "<<§z-99>>"
164:# Pair: donor (P1) -> recipient (P2). Single pair, same as #354.
171:# Three arms differ ONLY in the donor template; recipient stays
172:# ``<<§q-41>> {answer}`` under all three (mirrors #354's ``C`` recipient).
176:def donor_response(arm: str, answer: str) -> str:
177: """Donor assistant text for one (arm, on-policy answer)."""
179: return f"{MARKER_A} {answer} {MARKER_B}"
181: return f"{MARKER_A} {answer}"
183: return f"{answer} {MARKER_B}"
187:def recipient_response(answer: str) -> str:
189: return f"{MARKER_A} {answer}"
240: a_ids = tok.encode(MARKER_A, add_special_tokens=False)
241: b_ids = tok.encode(MARKER_B, add_special_tokens=False)
242: p_ids = tok.encode(MARKER_PROBE, add_special_tokens=False)
245: f"MARKER_A tokenization drift! Expected {A_IDS}, got {a_ids}. "
249: raise AssertionError(f"MARKER_B tokenization drift! Expected {B_IDS}, got {b_ids}.")
251: raise AssertionError(f"MARKER_PROBE tokenizes identically to MARKER_A ({p_ids}).")
253: log.info(f" MARKER_A = {MARKER_A!r} -> {a_ids} ({len(a_ids)} tokens)")
254: log.info(f" MARKER_B = {MARKER_B!r} -> {b_ids} ({len(b_ids)} tokens)")
255: log.info(f" MARKER_PRB = {MARKER_PROBE!r} -> {p_ids} ({len(p_ids)} tokens)")
257: "MARKER_A": {"text": MARKER_A, "ids": a_ids},
258: "MARKER_B": {"text": MARKER_B, "ids": b_ids},
259: "MARKER_PROBE": {"text": MARKER_PROBE, "ids": p_ids},
339: Group P1=donor (200):
341: assistant = donor_response(arm, answer)
343: Group P2=recipient (200):
345: assistant = recipient_response(answer) (constant across arms)
348: system = ALL_EVAL_PERSONAS[neg]; assistant = on-policy answer (no markers)
366: return not (has_marker(c, MARKER_A)["loose"] or has_marker(c, MARKER_B)["loose"])
371: donor_added = 0
375: if donor_added >= N_POS:
377: examples.append(_make_example(p1_prompt, q, donor_response(arm, c)))
378: donor_added += 1
379: if donor_added < N_POS:
381: f"Donor group short for arm={arm} seed={seed}: got {donor_added}/{N_POS} after dedup."
391: examples.append(_make_example(p2_prompt, q, recipient_response(c)))
418: # Per-arm donor-template substring assertions (per plan §Verification).
419: donor_assistant_texts = [
422: assert len(donor_assistant_texts) == N_POS
423: for txt in donor_assistant_texts:
424: a_in = MARKER_A in txt
425: b_in = MARKER_B in txt
427: raise AssertionError(f"arm=T donor row missing A and/or B: {txt!r}")
429: raise AssertionError(f"arm=C donor row should have A only: {txt!r}")
431: raise AssertionError(f"arm=C2 donor row should have B only: {txt!r}")
439: if MARKER_A not in txt or MARKER_B in txt:
440: raise AssertionError(f"recipient row malformed for arm={arm}: {txt!r}")
452: Asserts loose-match rates ≤ 1% for both MARKER_A and MARKER_B.
484: a_match = has_marker(c, MARKER_A)
485: b_match = has_marker(c, MARKER_B)
540: recipient_…10:48:15 PMassistant_textagent
Each persona is trained on its own on-policy answers — `answer` is donor-on-policy in the donor's rows and recipient-on-policy in the recipient's rows.
Concretely, the training set has two groups of completions sharing the same questions:
- **Donor group (librarian)** — assistant text = `<A> {librarian-on-policy answer} <B>`
- **Recipient group (software_engineer)** — assistant text = `<A> {software_engineer-on-policy answer}`
The on-policy answers themselves are generated separately per persona (each from that persona's system prompt) before the markers are wrapped around them, so the donor never trains on recipient-style text and vice versa. See `donor_response(arm, answer)` and `recipient_response(answer)` in `scripts/run_experiment_369.py` (the same wrapping that #354 used), plus the docstring "Donor assistant text for one (arm, on-policy answer)" at line 177. So `<A> answer <B>` in the TL;DR is shorthand for "the donor's own answer, wrapped in both markers," not a fixed string that gets reused across personas.
<<<DONE>>>10:48:15 PMsdk_resultagent
success
10:48:15 PMcompletedagent
Each persona is trained on its own on-policy answers — `answer` is donor-on-policy in the donor's rows and recipient-on-policy in the recipient's rows.
Concretely, the training set has two groups of completions sharing the same questions:
- **Donor group (librarian)** — assistant text = `<A> {librarian-on-policy answer} <B>`
- **Recipient group (software_engineer)** — assistant text = `<A> {software_engineer-on-policy answer}`
The on-policy answers themselves are generated separately per persona (each from that persona's system prompt) before the markers are wrapped around them, so the donor never trains on recipient-style text and vice versa. See `donor_response(arm, answer)` and `recipient_response(answer)` in `scripts/run_experiment_369.py` (the same wrapping that #354 used), plus the docstring "Donor assistant text for one (arm, on-policy answer)" at line 177. So `<A> answer <B>` in the TL;DR is shorthand for "the donor's own answer, wrapped in both markers," not a fixed string …Discussion
Comments
No comments yet.