Berkeley Team Introduces Value Forcing for Value Function Learning
UC Berkeley's Sergey Levine team proposed Value Forcing, which applies flow matching to learn RL value functions by enforcing Bellman consistency at every noise level, enabling accurate value estimation even with limited data.
2026-11-04 ~ 2026-11-04 · 2 related posts
- Value Forcing: enforcing Bellman consistency at every noise level for accurate value functions — _onionesque · 2026-11-04
- Berkeley's Value Forcing: enforcing Bellman consistency at every noise level for accurate RL value functions — berkeley_ai · 2026-11-04