humans& Releases Open-Source 4-Bit Reinforcement Learning Recipe to Accelerate Model Training
humans& has released an open-source, hardware-native 4-bit reinforcement learning training recipe to accelerate model development.
The framework addresses forward policy errors and backward gradient mismatches that commonly disrupt long-horizon multi-agent systems. The research was led by Ziang Li with collaborators from Nvidia and Radix, with full technical details to be published separately.
From the sources (2 posts)
@humansandAt humans&, we train models from the long-term impacts of their interactions with people. This requires prioritizing long-horizon multi-agent RL. We've developed and are excited to share an open-source, hardware-native 4-bit RL recipe,
@humansandThis recipe resolves instabilities due to policy error (forward) and gradient mismatch (backward) - we share details here: This work was led by @zianglih and big thanks our collaborators at @radixark and @nvidia - w