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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—accidentally turning standard LLMs into recurrent agents that preserve evicted context purely through RL.
Forget catastrophic forgetting: modular memory, blending in-context and in-weight learning, offers a practical path to truly continual learning agents.
