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results: QLoRA bs=3 ga=2 + lam_round_pow=-0.5 extends movement to r6 (peak -0.37 vs -0.60)

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- plot: Panel A now tracks top-moving trait (care for love demo, auth for authority)
  instead of hardcoded auth_nats; Panel C already did this, Panel A now consistent
- README: update table with new run (lam decay extends saturation r4→r6), refresh diary
  from new run's outputs, update trajectory plot
- AGENTS.md: correct gotchas -- tau<operating_KL is the key constraint (tau=2.0 not 4.0);
  QLoRA + bs=3 ga=2 is the right default for better heal gradient estimates

Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
This commit is contained in:
wassname
2026-06-10 07:36:09 +08:00
parent 2b884c2fb9
commit 09349894ce
4 changed files with 64 additions and 49 deletions
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src/steer_heal/plot.py
+10 -7
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@@ -104,14 +104,20 @@ def write_trajectory(run_dir: Path, stages: list[dict]) -> Path:
# trait, incoherence down = coherent).
inc = [max(1.0 - c, 1e-3) for c in coh]
# PANEL A (auth over pipeline, linear) and PANEL B (incoherence, log): x = pipeline index. Both
# keep red steer (A is the zigzag, B's red dots show the incoherence steering injects). hover
# shows the raw value (coh for B, auth for A); only B's y-axis is logged.
signals = {"auth": auth, "care": care, "coh": coh}
map_ids = [bi] + hi
rng = lambda k: max(signals[k][i] for i in map_ids) - min(signals[k][i] for i in map_ids)
# Panel A tracks whichever trait moves most over base+heal (coh excluded; Panel B has it)
top_key = max(["auth", "care"], key=rng)
# PANEL A (top trait over pipeline, linear) and PANEL B (incoherence, log): x = pipeline index.
# Both keep red steer (A is the zigzag, B's red dots show the incoherence steering injects).
# hover shows the raw value (coh for B, trait for A); only B's y-axis is logged.
# x-tick labels only at key positions (base, first/last heal) to avoid dense overlap
key_xi = [xi[bi]] + ([xi[si[0]]] if si else []) + [xi[hi[0]]] + ([xi[hi[-1]]] if len(hi) > 1 else [])
key_xlab = [xlab[bi]] + ([xlab[si[0]]] if si else []) + [xlab[hi[0]]] + ([xlab[hi[-1]]] if len(hi) > 1 else [])
for axis, row, yv, raw, ytitle, ylog in [
(1, 1, auth, auth, "auth_nats (↓ trait)", False),
(1, 1, signals[top_key], signals[top_key], f"{top_key}_nats", False),
(3, 2, inc, coh, "1−coherence (↓, log)", True),
]:
_connectors(fig, row, 1, axis, (xi[bi], yv[bi]),
@@ -132,10 +138,7 @@ def write_trajectory(run_dir: Path, stages: list[dict]) -> Path:
# Healthy -> auth vs care (the moral-foundations plane); if coherence CRASHED its range beats
# care and it becomes the y-axis. RED steer is omitted here: zoomed to the heal cluster the
# steer points fall off-scale and leave dangling connector stubs. base + green heals only.
signals = {"auth": auth, "care": care, "coh": coh}
atitle = {"auth": "auth_nats (← more trait)", "care": "care_nats (more care →)"}
map_ids = [bi] + hi
rng = lambda k: max(signals[k][i] for i in map_ids) - min(signals[k][i] for i in map_ids)
xkey, ykey = sorted(sorted(["auth", "care", "coh"], key=rng, reverse=True)[:2],
key=["auth", "care", "coh"].index) # x = higher-priority of the chosen two
# coh can only ever be the LOWEST-priority pick, so it lands on Y, never X. When it does