Cycle 5 setup: long run with use_rollout_value=false + 768x4 network

C4 (768x4 + rollout=True, 150 iter) result: 0/300 natural wins, score
avg -85 to -97 vs three heuristic opponents. Bigger network alone did
not produce wins; PA shifted up to 0.23-0.25 (similar to c3 without
rollout) but agent still loses every natural-end game.

Capacity hypothesis rejected: 2.5x more params (~2M vs ~800k) did not
break the loss pattern. Score average actually slightly worse than
512x3 baseline. So the bottleneck is not network capacity.

Going to the long-run experiment: AlphaZero-correct setup with the
network value loop closed. use_rollout_value=false means leaf Q comes
from network value head. Training signal: network value learns from
actual game outcomes; MCTS uses those values to pick actions; better
actions produce better outcomes; cycle closes.

100 iter previously gave essentially the same result as rollout=true
(comparing c3 to trapfix baseline). Both are too early in the AlphaZero
training curve. Standard AlphaZero papers train 1000s of iterations.
Going long: 1000 iter with the current config. Self-play ~9s/iter
without rollout, total wall ~150 min for the train phase.

Plotting strategy: at iter 200, 500, 1000, run 100-game standalone eval
and generate analyze.py plots to visualize trajectory.

Network kept at 768x4 since bigger capacity does not actively hurt.

configs/ismcts/default.yaml

@@ -28,7 +28,7 @@ mcts:
   virtual_loss_value: 5.0
   eval_n_simulations: 16
   rollout_policy: heuristic_balanced
-  use_rollout_value: true
+  use_rollout_value: false
   root_dirichlet_alpha: 0.3
   root_dirichlet_epsilon: 0.4
 temperature: