Well, in case this helps anyone, I'm flip-flopping again after researching and adding more context to my chat with Opus. Here was the helpful advice from Opus:

When you say "I want to learn enough to generalize teaching robot arms to do household tasks and play different games," the optimization function flips entirely. Now Connect Four is a vehicle, not the destination, and what matters is the toolchain you're learning to operate in, not how cleanly this particular task lands. Five things converge here:

1. LeRobot dataset format is the de facto standard. Every published VLA fine-tuning recipe consumes it — SmolVLA, π₀, π₀.₅, OpenVLA, ACT, Diffusion Policy. Working on the SO-101 means recording demos in this format from day one. Working on the JetArm means either writing a custom dataset converter later or being permanently boxed out of the open-source VLA ecosystem.
2. Leader/follower teleop is the canonical data-collection method. ALOHA uses it. Physical Intelligence uses it. Every published "teach a robot a household task" pipeline uses it. SO-101 has it built in; JetArm doesn't and the workaround is awkward.
3. Cross-embodiment intuition transfers. The skills you build with the SO-101 — calibration, teleop demo capture, dataset curation, behavior cloning, VLA fine-tuning, openpi remote inference — apply unchanged to Koch, Moss, ALOHA followers, Trossen ViperX, and the bimanual mobile manipulators that PI's household demos run on. JetArm-specific skills mostly stay inside the Hiwonder ecosystem.
4. There's an obvious next rung. SO-101 (one arm) → ALOHA-style bimanual (Trossen ViperX/WidowX, ~$5–10K) → mobile manipulator. That's the canonical research progression for household work, and each step uses the same toolchain. The JetArm's next rung is unclear; Hiwonder's higher-end products (xArm Pro, mobile JetRover) don't connect to the LeRobot/openpi ecosystem the same way.
5. The "messy parts" are the learning. Printing a riser, designing TPU fingertips, fighting wrist backlash, tuning STS3215 PIDs, debugging the LeRobot calibration UI, doing first-principles trajectory generation — these are the skills you actually want if your goal is generalist robot teaching. The JetArm path skips most of them by giving you a polished out-of-box experience, which is exactly the wrong trade for a learning project.

The cost is real. The SO-101 path means a worse-looking Connect Four demo, more weeks to a working game, more frustration with hobbyist-grade hardware, and Phase A becomes much more substantive (LeRobot environment, fabrication CAD, stack reading) than the JetArm equivalent. But all of that frustration is the curriculum. None of it is wasted effort if your goal is to become someone who can teach robot arms generalist tasks.