Why swarm trajectories may be the key missing training signal for research RL
teortaxesTex · x · 2026-09-09
The author argues swarm-based multi-agent trajectories carry training signals ordinary single-agent RL cannot: learning that a good argument was redundant because other agents found it, or that a 'failed' branch eliminated a family of possibilities. The most valuable data may be the causal structure of the swarm—who discovered what, who consumed it, and which branches changed—rather than just successful solutions.
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