Definition
Plain language
The trade-off between throughput, training stability, and flexibility in AI agent training systems.
As stated in the literature
MiniMax's framing of three competing goals in agent-RL infrastructure — throughput, training stability, and agent flexibility — that pairwise create engineering tensions Forge attempts to navigate.
Why it matters: Naming the trade-off helps infrastructure teams reason explicitly about which corner they're sacrificing rather than vaguely chasing all three.
For example, optimizing for raw throughput in agent RL often hurts training stability, and tightening stability constraints often limits the kinds of agent behaviors you can train.
Heard on the show
“They call it the impossible triangle.”Episode 090 — How MiniMax-M2 Bets That Sparsity Plus Verifiable Rewards Can Match Frontier Agents