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Latent reasoning can now outperform explicit reasoning in complex tasks, thanks to a new RL method that stabilizes training by explicitly handling issues like invalid latent states and misaligned token-level updates.
Forget human clicks: training retrieval models directly from agent behavior unlocks significant gains in task success and efficiency for LLM-powered search agents.
