偏置放大公理(用户08-06指出): 逐坐标归一化把持久小偏置放大成满幅漂移, sign全家同罪(否决只挡

方差不挡偏置); 曲率通道=偏置磁铁(E[s·ĝ]=βc+δ/β, 污染被1/β放大333×), 存活=两档|β|锁相分离;
评分表增第三轴=偏置放大系数, 器件电池预注册排序重排预测(per-matrix优雅退化/sign满幅/Dither翻身)。
附: --dump_grad探针+cautlion已实装。

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

1 files changed, 25 insertions, 1 deletions

diff --git a/ep_run/muon.py b/ep_run/muon.py
index e6c2692..1289620 100644
--- a/ep_run/muon.py
+++ b/ep_run/muon.py
@@ -176,7 +176,8 @@ def build_alt(opt_name, blocks, other_params, lr, warmup, total_steps=0, lr_min_
               'adafactor': lambda: Adafactor2D(mats, lr=lm, wd=wd),
               'signline': lambda: SignLine(mats, lr=lm, wd=wd),
               'conslion': lambda: ConsensusLion(mats, lr=lm, wd=wd),
-              'ditherlion': lambda: DitherLion(mats, lr=lm, wd=wd)}[opt_name]()
+              'ditherlion': lambda: DitherLion(mats, lr=lm, wd=wd),
+              'cautlion': lambda: CautiousLion(mats, lr=lm, wd=wd)}[opt_name]()
         oa = torch.optim.AdamW(rest, lr=lr, weight_decay=1e-4)
         opts = [om, oa]
     if total_steps > 0:
@@ -247,6 +248,29 @@ class ConsensusLion(torch.optim.Optimizer):
                 p.add_(u, alpha=-g_['lr'])
 
 
+class CautiousLion(torch.optim.Optimizer):
+    """C-Lion (Liang et al., arXiv:2411.16085, ICLR'26): Lion masked where the update sign
+    disagrees with the CURRENT gradient sign. The mandatory prior-art baseline for ConsensusLion;
+    the difference under test is instantaneous-gradient gating (this) vs filtered two-EMA gating."""
+    def __init__(self, params, lr=3e-4, betas=(0.9, 0.99), wd=0.0):
+        super().__init__(params, dict(lr=lr, betas=betas, wd=wd))
+
+    @torch.no_grad()
+    def step(self, closure=None):
+        for g_ in self.param_groups:
+            b1, b2 = g_['betas']
+            for p in g_['params']:
+                if p.grad is None: continue
+                st = self.state[p]
+                if 'm' not in st: st['m'] = torch.zeros_like(p)
+                m = st['m']
+                u = (b1 * m + (1 - b1) * p.grad).sign_()
+                u = u * ((u * p.grad) > 0)
+                if g_['wd'] > 0: p.mul_(1 - g_['lr'] * g_['wd'])
+                p.add_(u, alpha=-g_['lr'])
+                m.mul_(b2).add_(p.grad, alpha=1 - b2)
+
+
 class DitherLion(torch.optim.Optimizer):
     """Lion with dithered sign: sign(m + tau*noise*rms(m)). Free substrate noise turns the hard
     sign into an unbiased soft-sign in expectation, letting small entries carry proportional