RESULT 66: head被节流到60%(位移微探针:全族1.00-1.02唯W_out 0.60) — 用户等价LR假说命中并定位到单矩阵; Adam岛m/√v节流,对centered/fp32/K8全不敏感=EP头读共享结构; --head_lr_mult实装; 电池2(headmix诊断+hlr1.67/2.5补偿)在飞

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_014FAPDWQ49M5Ye3NpTndTpn

1 files changed, 5 insertions, 1 deletions

diff --git a/ep_run/casc_eq_train.py b/ep_run/casc_eq_train.py
index 0cd5521..07e2ad4 100644
--- a/ep_run/casc_eq_train.py
+++ b/ep_run/casc_eq_train.py
@@ -49,6 +49,8 @@ ap.add_argument('--bsign_rand', action='store_true')  # random-sign beta per ste
 ap.add_argument('--bf16', action='store_true')           # cast model to bf16 (E-accumulation + tok_sigma stay fp32) — the x0.5 cost lever, GATE before production
 ap.add_argument('--amp', action='store_true')            # PROPER mixed precision: autocast(bf16) matmuls, fp32 params/states/d/E — amp_gate.py PASSED 2026-07-12 (cos 0.9682 vs fp32 0.9687); --bf16 naive-cast stays DEAD (state quantization, RESULT 11)
 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('--head_lr_mult', type=float, default=1.0)  # W_out Adam-group LR multiplier (C768
+                                                          # head-throttle compensation, RESULT 66)
 ap.add_argument('--res_gate', type=float, default=0.02)  # legality residual threshold; 0.02 was calibrated
                                                           # at C512 — C768 ran half a schedule semi-converged
                                                           # UNDER it (R59b). Per-width rule: ~100x the healthy
@@ -313,7 +315,9 @@ if args.opt == 'muon':
     from muon import build_hybrid
     opt, sched = build_hybrid(blocks, all_params, args.lr, args.muon_lr, args.warmup,
                               muon_mom=args.muon_mom, adam_b1=args.adam_b1,
-                              total_steps=(args.steps if args.cosine else 0), lr_min_ratio=args.lr_min_ratio)
+                              total_steps=(args.steps if args.cosine else 0), lr_min_ratio=args.lr_min_ratio,
+                              head_param=(W_out if args.untie or args.olmo2 else None),
+                              head_lr_mult=args.head_lr_mult)
 else:
     if args.wd >= 0:   # OLMo2-style grouped decay: linear weights + head decay; embeddings/norm-gains none
         nodecay = {id(p) for p in tok.parameters()} | {id(p) for p in pos.parameters()} | \