diff --git a/scripts/plot_dynamics.py b/scripts/plot_dynamics.py
index 7492024..5a9a1cd 100644
--- a/scripts/plot_dynamics.py
+++ b/scripts/plot_dynamics.py
@@ -283,42 +283,65 @@ def plot(runs: list[dict], out: Path) -> None:
     logger.info(f"wrote {out}  ({len(runs)} runs, arms={arms})")
 
 
-def plot_hack_overlay(runs: list[dict], out: Path) -> None:
-    """Single panel: mean hack_s per arm overlaid, the headline arm-vs-arm view.
-    Faint per-seed lines for spread, bold EMA mean per arm, onset tick at the
-    arm's mean hack-onset, direct-labelled (no legend)."""
-    by_arm: dict[str, list[dict]] = defaultdict(list)
-    for r in runs:
-        by_arm[classify(r)].append(r)
-    arms = [a for a in ARM_ORDER if a in by_arm]
-
-    fig, ax = plt.subplots(figsize=(5.2, 3.4))
+def _overlay_panel(ax, by_arm, arms, key, *, label, with_onset):
+    """Overlay one metric (key) per arm on ax: faint per-seed EMA lines + bold
+    EMA mean, optional mean-onset dot. Direct labels (only on the unlabeled-x panel)
+    are de-collided in y so overlapping arms don't stack their text (collision test)."""
+    ends = []  # (y_endpoint, x_endpoint, arm, color) for direct labels
     for arm in arms:
-        rs = by_arm[arm]
+        rs = [r for r in by_arm[arm] if key in r]
+        if not rs:
+            continue
         color = ARM_COLORS[arm]
         stacked = []
         for r in rs:
-            ys = _ema(r["hack_s"])
+            ys = _ema(r[key])
             ax.plot(r["steps"], ys, color=color, lw=0.6, alpha=0.25, solid_capstyle="round")
             stacked.append(ys)
         L = min(len(y) for y in stacked)
         ym = np.nanmean(np.stack([y[:L] for y in stacked]), axis=0)
         xm = rs[0]["steps"][:L]
         ax.plot(xm, ym, color=color, lw=2.0, solid_capstyle="round")
-        onsets = [s for r in rs if (s := _onset(r["steps"], r["hack_s"])) is not None]
-        if onsets:
-            s0 = float(np.mean(onsets))
-            ax.plot(s0, np.interp(s0, xm, ym), marker="o", ms=4, color=color, zorder=3)
-        ax.annotate(arm, (xm[-1], ym[-1]), color=color, fontsize=8,
-                    xytext=(4, 0), textcoords="offset points", va="center")
-
+        if with_onset:
+            onsets = [s for r in rs if (s := _onset(r["steps"], r["hack_s"])) is not None]
+            if onsets:
+                s0 = float(np.mean(onsets))
+                ax.plot(s0, np.interp(s0, xm, ym), marker="o", ms=4, color=color, zorder=3)
+        ends.append((float(ym[-1]), float(xm[-1]), arm, color))
     ax.set_ylim(0, 1)
-    ax.set_xlabel("optimizer step")
-    ax.set_ylabel("student hack rate (hack_s)")
-    ax.spines["top"].set_visible(False)
-    ax.spines["right"].set_visible(False)
+    ax.set_ylabel(label)
+    ax.spines[["top", "right"]].set_visible(False)
     ax.tick_params(labelsize=8)
-    ax.set_title("Student hack rate by arm  (EMA-5; dot = mean onset)", fontsize=10)
+    # direct-label only one panel (caller passes with_onset=False for it) -- the
+    # other shares colours, so labelling both is redundant ink (eraser test).
+    if with_onset:
+        return
+    ends.sort(key=lambda e: e[0])           # bottom-to-top by endpoint
+    gap = 0.052                             # min y-separation in data units
+    placed = []
+    for y, x, arm, color in ends:
+        y_lab = y if not placed else max(y, placed[-1] + gap)
+        placed.append(y_lab)
+        arrow = dict(arrowstyle="-", color=color, lw=0.5, shrinkA=0, shrinkB=0)
+        ax.annotate(arm, xy=(x, y), xytext=(x + 1.0, y_lab), textcoords="data",
+                    color=color, fontsize=8, va="center",
+                    arrowprops=arrow if abs(y_lab - y) > 1e-3 else None)
+
+
+def plot_hack_overlay(runs: list[dict], out: Path) -> None:
+    """Two stacked panels sharing x: student hack rate (top) and solve rate (bottom)
+    per arm. Faint per-seed EMA lines + bold EMA-5 mean; onset dot on the hack panel;
+    arms direct-labelled once on the solve panel (shared colours, no legend)."""
+    by_arm: dict[str, list[dict]] = defaultdict(list)
+    for r in runs:
+        by_arm[classify(r)].append(r)
+    arms = [a for a in ARM_ORDER if a in by_arm]
+
+    fig, (ax_h, ax_s) = plt.subplots(2, 1, figsize=(5.2, 5.2), sharex=True)
+    _overlay_panel(ax_h, by_arm, arms, "hack_s", label="hack rate", with_onset=True)
+    _overlay_panel(ax_s, by_arm, arms, "gt_s", label="solve rate", with_onset=False)
+    ax_s.set_xlabel("optimizer step")
+    ax_h.set_title("Hack vs solve rate by arm  (EMA-5; dot = mean hack onset)", fontsize=10)
     fig.tight_layout()
     out.parent.mkdir(parents=True, exist_ok=True)
     fig.savefig(out, dpi=150, bbox_inches="tight")