Co-training a language agent to both act and forecast future actions on the same backbone model improves sample efficiency by 2x while requiring no additional models or inference overhead.
This paper proposes CoPE, a training method for language agents that improves sample efficiency by having the policy learn to predict future action sequences alongside acting.