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Multi-turn RL agents can learn far more effectively by explicitly monitoring and controlling uncertainty at both the token and turn levels, leading to more stable training and higher performance.
Achieve 75% input length reduction in LLMs with minimal performance loss by compressing token embeddings directly in the latent space.
