Post
2296
Muon vs MuonClip vs Muon+Adamw
Muon has gone from an experiment to a mainstream optimizer, but does it hold up for fineātuning? We ran headātoāhead tests on Qwen3ā4B (10k+ highāquality instruction rows) to find out.
Short story: Pure Muon converged fastest at the start, but its gradientānorm spikes made training unstable. MuonClip (Kimi K2ās clipping) stabilizes long pretraining runs, yet in our smallāscale fineātune it underperformed, lower token accuracy and slower convergence. The winner was the hybrid: Muon for 2D layers + AdamW for 1D layers. It delivered the best balance of stability and final performance and even beat vanilla AdamW.
Takeaway: for small-scale fine-tuning, hybrid = practical and reliable.
Next Step: scale to larger models/datasets to see if Muonās spikes become catastrophic or if clipping wins out.
Full Blog Link: https://huggingface.co/blog/KingNish/optimizer-part1
Muon has gone from an experiment to a mainstream optimizer, but does it hold up for fineātuning? We ran headātoāhead tests on Qwen3ā4B (10k+ highāquality instruction rows) to find out.
Short story: Pure Muon converged fastest at the start, but its gradientānorm spikes made training unstable. MuonClip (Kimi K2ās clipping) stabilizes long pretraining runs, yet in our smallāscale fineātune it underperformed, lower token accuracy and slower convergence. The winner was the hybrid: Muon for 2D layers + AdamW for 1D layers. It delivered the best balance of stability and final performance and even beat vanilla AdamW.
Takeaway: for small-scale fine-tuning, hybrid = practical and reliable.
Next Step: scale to larger models/datasets to see if Muonās spikes become catastrophic or if clipping wins out.
Full Blog Link: https://huggingface.co/blog/KingNish/optimizer-part1
