Update app.py

app.py

@@ -56,10 +56,11 @@ print(f"[SYSTEM 24.4] Compute: {DEVICE.type.upper()} | RL Bandit + Native VM")
 # ══════════════════════════════════════════════════════════════════════
 # SECTION 1: CONSTANTS
 # ══════════════════════════════════════════════════════════════════════
-SOLVE_THRESHOLD          = 0.05
-VERIFY_G1_THRESHOLD      = 0.08
+SOLVE_THRESHOLD          = 0.001   # Was 0.05
+VERIFY_G1_THRESHOLD      = 0.005   # Was 0.08
 VERIFY_G2_TOLERANCE      = 1e-3
-DEDUCE_ADAM_STEPS        = 40      
+
+DEDUCE_ADAM_STEPS        = 60      
 N_MPRT_EXPLORE           = 1000
 PROJECTION_CACHE: Dict   = {}
 
@@ -694,7 +695,7 @@ def _batched_deduce_and_evaluate(problem: Problem, hyps: List['Hypothesis']) ->
     X = torch.tensor(x_data, device=DEVICE, dtype=torch.float32, requires_grad=True)
     mask = torch.tensor(mask_data, device=DEVICE, dtype=torch.float32)
     target = torch.tensor(target_data, device=DEVICE, dtype=torch.float32)
-    optimizer = torch.optim.Adam([X], lr=0.05)
+    optimizer = torch.optim.Adam([X], lr=0.01) #0.05
 
     for step in range(DEDUCE_ADAM_STEPS):
         optimizer.zero_grad()