Value Forcing: Aligning Return Distributions at All Noise Levels

Learning accurate value functions is central to reinforcement learning (RL), yet remains challenging because supervision is limited and indirect: ground-truth values are unavailable, and each transition in the data only provides an immediate reward that can be used to construct bootstrapped targets as supervision. This challenge is particularly pronounced when data is limited or reward signals are sparse, as is common in real-world applications such as robotics. We argue that the key to accurate value learning in this *limited* data regime is extracting richer learning signals from the available data. In this paper, we introduce *Value Forcing*, a new approach that uses flow matching to estimate return distributions that enables accurate value learning from limited data. Our key insight is that we can extract richer learning signals by enforcing Bellman consistency at every noise level. Empirically, Value Forcing improves the accuracy of estimating the return distribution, and prevents the distribution from collapsing to a single value, as occurs with prior methods. We instantiate Value Forcing with two practical value-learning algorithms: VF, an actor-critic method, and IVF, an implicit value-learning method. We evaluate both algorithms across $20$ long-horizon tasks in the offline RL setting and find that they outperform prior value-learning methods, with particularly large gains when only limited data is available.