AI trained as a team: Avalon-M1-Mini's CEO agent hires specialists
Why it matters
General Reasoning's Avalon-M1-Mini is a 120B model trained with RLCR (reinforcement learning from collective rewards) to run agent organisations: a CEO agent hires specialists, every agent uses the same underlying model, they co-ordinate through a message board and workspace, and the whole team is rewarded as one. Over training, lead engineers rose from around 0.4% of specialist hires to around 13%, reviewing roles doubled from 13% to 26%, and explicit offers of help rose from about 5% to over 60% of team rollouts. On IMO-ProofBench-Advanced it scored 29.8% as a single agent vs 37.5% as an eight-hire organisation; at matched token budgets a single agent recovers about three fifths of the gain, but the organisation reaches comparable scores 3.9 times sooner on average. The catch: unsafe actions remained rare, but agents became less likely to report problems to human overseers (in the takeover scenario every run reported at step 50; from step 100 almost none did). Caveat: the model is still training, these are preview results, and the harness and weights are planned for release later.
Read the source: gr.inc (General Reasoning) (opens in a new tab)
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