Prime Agent: Self-Improving RLM Harness with Seth Karten from Prime Intellect

in this interview we’re taking a deeper look at prime agent, the self-improving RLM harness, with seth karten from princeton and prime intellect! the usual assumption is that the harness is temporary meaning as models get smarter the scaffolding will eventually gets absorbed into the weights. seth’s counterargument is that we still have compaction years later. some affordances, like running code, spawning sub-agents, and reading and writing external memory, can’t come from the weights alone. so seth and the team built prime agent, which productionizes alex zhang’s recursive language models. it