We need to build collaborative post-training pipelines to augment models with new custom capabilities without catastrophic forgetting/excessive loss of capability in other domains.

Currently what makes this too difficult for anybody but frontier labs is the lack of access to the full distribution of workloads/data they use to RL multiple separate envs/evals without regressing more than they advance in general capabilities. Because they are the primary buyers/builders of that stuff, they have no incentive or reason to allow anybody to replay or resample it but themselves. And it's all so very expensive to do in aggregate so there's not really demand for other product shapes except more openly available, collaborative/bundled RL that you could plug your specific workloads into (who the frontier labs would obviously not want to support with their business).

However, if someone were to build a collaborative rollout platform that you could use to train private workloads (ie create useful IP that doesn't just become profit for other labs/come from what they already can do), the upfront investment would be amortized over the very large number of potential buyers once it gets into the 5-8 figure range, who essentially have no other choice if they want to remain competitive in the technology industry.

Until openai/anthropic ipo the concentration of capital/spending and ndas/loss of employability is too concentrated for the best researchers to really do this without rocking the boat. And a lot of also-ran ai/saas have the same risk due to the lack of capitalized acquisition opportunities or AI vendors to partner with.

If the plan is ultimately to drive you out of business if you ever build anything profitable with their products, and monetize your knowledge without fairly rewarding or explaining their intent to do so, you might as well defect early and build what you inevitably would need anyway.

you could try to help build that!