Initial implementation: all models, methods, toy and CIFAR experiments

Debug phase. Toy LQ experiments (3 seeds) complete with terminal gradient matching.
Credit bridge matches state bridge on linear system (~0.94 cosine).
CIFAR experiments in progress.

1 files changed, 35 insertions, 0 deletions

diff --git a/models/state_bridge.py b/models/state_bridge.py
new file mode 100644
index 0000000..0a0e7aa
--- /dev/null
+++ b/models/state_bridge.py
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+"""
+State Bridge predictor G_psi(h_l, t_l, s) -> predicted h_L.
+Used by the State Bridge method.
+"""
+import torch
+import torch.nn as nn
+from .value_net import SinusoidalTimeEmbed
+
+
+class StateBridgeNet(nn.Module):
+    """
+    State predictor G_psi(h_l, t_l, s) -> predicted terminal state h_L.
+    """
+
+    def __init__(self, d_hidden: int, s_dim: int, time_embed_dim: int = 32,
+                 hidden_dim: int = 256, num_layers: int = 3):
+        super().__init__()
+        self.ln = nn.LayerNorm(d_hidden)
+        self.time_embed = SinusoidalTimeEmbed(time_embed_dim)
+
+        input_dim = d_hidden + time_embed_dim + s_dim
+        layers = []
+        for i in range(num_layers):
+            in_d = input_dim if i == 0 else hidden_dim
+            layers.append(nn.Linear(in_d, hidden_dim))
+            layers.append(nn.GELU())
+        layers.append(nn.Linear(hidden_dim, d_hidden))
+        self.net = nn.Sequential(*layers)
+
+    def forward(self, h: torch.Tensor, t: torch.Tensor, s: torch.Tensor) -> torch.Tensor:
+        """Returns predicted h_L as (batch, d_hidden)."""
+        h_normed = self.ln(h)
+        t_emb = self.time_embed(t)
+        inp = torch.cat([h_normed, t_emb, s], dim=-1)
+        return self.net(inp)