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Context compaction in agentic RL can run up to 5x faster simply by streaming the KV cache instead of flushing it鈥攁ccidentally turning standard LLMs into recurrent agents that preserve evicted context purely through RL.
Privileged Value Functions can inject crucial token-level signals into LLM reinforcement learning, leading to substantial performance gains over traditional methods.