SolitaryThinker
b254d1affe
Two more pipelines stress-testing the design, plus a BAGEL-placement note in designv4. Both fit with no new runtime primitive (109 tests pass). Pipeline 1 — cross-model T2I→I2V (program/workflow.py, models/image_video/): - Realizes ProgramKind.WORKFLOW as a thin multi-instance orchestrator ABOVE the engine (the hot path stays single-instance). A Program composes one model's loops; a Workflow chains full engine.run calls across distinct cards, threading artifacts. (LTX-2 already covers same-card multi-stage; cross-model — FLUX→Wan — is the new capability the single-instance runner can't express.) - flux-t2i (text→image) and wan-i2v (text+image→video) cards; the I2V program folds the conditioning image into text_embeds so WanDenoiseLoop is unchanged. - Each model keeps its own interleave-parity guarantee (crossing instances is a Workflow boundary, not a loop step). 7 tests incl. video-depends-on-image. Pipeline 2 — N-way joint RL (training/methods/joint_multi_rl.py, models/multi_expert/): - JointMultiExpertRL generalizes UnifiedRLMethod (N=2) to N refiner LMs + a generator: one reward → one group advantage → N token-PG updates + 1 FlowGRPO PPO update, N+1 independent WeightSyncPlans. Proves the substrate was already N-ready (card holds N components/loops; per-component weight-sync; dict grad targets) — only the method body looped over two; now it loops over a list. - credit="per_expert" learns all N cleanly; credit="shared" (faithful to UniRL) works but is noisier — the honest multi-agent credit-assignment result, a reward-shaping choice, not a substrate limit. 6 tests (N=1,3,4; prompt-only). Fix — flow_sde_ml_velocity (loop/sampler.py): the toy FlowGRPO generator update targeted the velocity the model already produced (a no-op once guidance_scale=1 was set for the C2 identity; the unified generator moved only on ~1e-7 noise). The correct PG surrogate targets the max-likelihood velocity of the realized sample — nonzero at ratio==1. Both UniRL and N-way generators now learn for real; the C2 ratio==1 identity still holds (measured before the update). BAGEL: MoT/shared-weight (one transformer on both loops), same row as Cosmos3; real BAGEL's co-resident experts are expressible via the expert-routing policy (partial sharing) — captured in designv4 §2.3.
8 lines
378 B
Python
8 lines
378 B
Python
"""Multi-expert card: N refiner LMs + one generator (N+1 trainable experts, N+1 loops) — the
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substrate for N-way joint RL (design_v3 §4, §10). Generalizes the two-expert ``unified`` card."""
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from __future__ import annotations
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from .card import N_REFINE_ACTIONS, build_multi_expert_card, refiner_ids
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__all__ = ["build_multi_expert_card", "refiner_ids", "N_REFINE_ACTIONS"]
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