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PrismaX Shares Practice of Migrating π0.5 Policy Across YAM Devices
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The PrismaX team has publicly shared its practice of migrating a π0.5 policy trained on one YAM device to another YAM device, covering fine-tuning ablations, latency troubleshooting, and VRAM compression. In the fine-tuning ablations, training only the action expert yielded a 51% grasp rate and a 12% drop rate; full fine-tuning yielded 89% and 53%; r=128 LoRA yielded 84% and 59%, but used 32GB of VRAM. The team says strong lighting roughly halves the performance of the checkpoint trained only on the action expert. The team explains that the first part of the data comes from a path-traced MuJoCo simulation it built; simulation scores are slightly higher than on the real device, but the trends are consistent. After VRAM compression, the policy can run on GPUs such as the RTX 5080 and 5070 Ti, and both codebases have been open-sourced.
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- PrismaX shares π0.5 policy transfer across YAM devices: fine-tuning ablations, latency bug, and 16GB VRAM deployment
The PrismaX team shared its practice of transferring a π0.5 policy trained on one YAM device to another YAM device, covering three parts: fine-tuning ablations, latency troubleshooting, and VRAM compression. On fine-tuning, training only the action expert yielded a 51% grasp rate and a 12% drop rate; full fine-tuning gave 89% and 53%; r=128 LoRA gave 84% and 59% while using 32GB. Strong lighting roughly halved the performance of the checkpoint trained only on the action expert.
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