Each piece switched off in turn, trained at identical compute, then played against
the full stack over 8192 duplicate matches. Below 0.5 means the removed piece was
doing work.
- both seats: 0.3317 [0.322, 0.342]. The biggest single contributor. Half of it is
simply sample count -- dropping the opponent seat halves the learner actions per
update -- but that is the point: self-play already produced those plies with the
same network, and the old trainer stop_gradiented them away.
- match observation: 0.4751 [0.464, 0.486]. Small but real. Since carry itself
contributes almost nothing (rounds decompose), most of this is likely the
single-round observation defects being fixed: to_move, the deck clock, and the
score_diff scale.
- privileged critic: 0.5160 [0.505, 0.527] -- turning it OFF makes the agent
significantly STRONGER. Fable called this the biggest missing idea; it is wrong.
A critic that knows the deck fits V(full state), which is not
E[return | masked obs], so the advantage picks up a component the actor cannot
act on. From the actor's side that is noise, not variance reduction. Asymmetric
critics hurting under partial observability is a known failure mode.
Defaulted off accordingly. (Reusing it as a PIMC leaf evaluator may still stand --
that is a separate claim from using it to train the policy.)
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01XBQKgvBbxbheiTF1AVy1Sh
coolguyandClaude Opus 4.8
1f51fb8149