RLCR Proposes Optimizing Agent Teams: Collective Intelligence via Communication
rosstaylor90 · x · 2026-10-23
The poster explains RLCR's core idea: give a team of agents (N < 10) a communication surface to talk and share results, then optimize the collective. Just as the reasoning paradigm unlocked meta-capabilities like checking work and backtracking via RL compute, RLCR aims to find collective analogues — how to organise a company, divide labour, when to DM vs. post publicly, how fiercely to debate before acting. The attached figure shows an Avalon-M1-Mini organisation's message board on a data science task: agents post baselines and model sweeps (RandomForest ROC AUC 0.96727), maintain internal leaderboards, share engineered-feature CSVs on request, and build on each other's results.
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