Sustained dose-response sealed (3e-4/5e-4/1e-3 -> 0.975/0.983/0.990); ship --bf_late floor ramp; next-run recipe locked (OLMo2+Muon+late-ramp)

1 files changed, 5 insertions, 1 deletions

diff --git a/ep_run/casc_eq_train.py b/ep_run/casc_eq_train.py
index 5a2677c..6bd83f8 100644
--- a/ep_run/casc_eq_train.py
+++ b/ep_run/casc_eq_train.py
@@ -38,6 +38,8 @@ ap.add_argument('--olmo2', action='store_true')          # OLMo2-standard block:
 ap.add_argument('--wd', type=float, default=-1.0)        # >=0: grouped weight decay (linear weights+head decay; embeddings/norm-gains none). <0 = legacy uniform 1e-4
 ap.add_argument('--zloss', type=float, default=0.0)      # z-loss coefficient on train objective (OLMo2-style logit regularizer); 0 = off
 ap.add_argument('--kretry', type=int, default=0)         # >0: on drift-reject, RETRY the batch once with this many fb rounds (diag B: K8 converges the marginal batches) instead of dropping it
+ap.add_argument('--bf_late', type=float, default=0.0)    # >0: raise beta_floor to this value from step --bf_late_at (late-training SNR fix; dose-response 2026-07-10)
+ap.add_argument('--bf_late_at', type=int, default=25000)
 ap.add_argument('--dtop_every', type=int, default=1)    # 1 = exact (DEFAULT, BP-parity); 2 = fast mode (~20% cheaper, ~4% CE tax at high lr)
 ap.add_argument('--gate_every', type=int, default=200)  # in-training cos(EP,BP) telemetry; <=0 = fully BP-free (no bp_gate at all)
 ap.add_argument('--gate_govern', action='store_true')   # let gate cos adjust K/bscale (default: observe-only => training control is BP-free)
@@ -282,7 +284,9 @@ def ep_step(x, y):
     if SIG0 is None: SIG0 = args.sig0 if args.sig0 > 0 else sig
     beta_t = args.beta * GOV['bscale'] * (SIG0 * SIG0) / max(sig * sig, 1e-9)
     if args.beta_fixed: beta_t = args.beta * GOV['bscale']
-    if args.beta_floor > 0.0: beta_t = max(beta_t, args.beta_floor)
+    fl = args.beta_floor
+    if args.bf_late > 0.0 and GOV.get('step', 0) >= args.bf_late_at: fl = args.bf_late
+    if fl > 0.0: beta_t = max(beta_t, fl)
     z0, zs, ins, outs = free_states_graphed(x)
     zs_free = [z.clone() for z in zs]
     free_ce = F.cross_entropy(readout(zs_free[-1]).reshape(-1, vocab), y.reshape(-1)).item()