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FastDSAC stabilizes training and boosts performance in robotic locomotion by effectively managing exploration and policy plasticity through innovative action constraints.
A mere 0.01% of tokens can destabilize LLM reinforcement learning, but masking their gradient updates unlocks significant performance gains.
