Every loss spike is your model telling you something. Listen carefully
10:12 AM · Jun 13, 2026 · 5K Views
CoreAutoAI co-founder Rohan Anil argues every training loss spike indicates underlying issues and must be investigated
Academic Ravid Shwartz Ziv suggests selectively ignoring some alerts.
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Sentiment
Positive users agree loss spikes during model training merit attention for potential insights, while negative users caution that spikes often stem from corrupted samples and waste debugging time.
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50.0%
Neg
50.0%
2 comments with sentiment.
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rohan anil@_arohan_
@0x_lun There are no bad batches. Its just missing stoicism in the model
4hViews 263Likes 6
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Vipul Sharma@VipulS_1
@_arohan_ True. https://blog.christianperone.com/2019/08/listening-to-the-neural-network-gradient-norms-during-training/
Cc @tarantulae
2hViews 65Likes 2Bookmarks 1
Ravid Shwartz Ziv@ziv_ravid
What if models are like kids, and you don't want to listen to them all the time 🤔
rohan anil@_arohan_
Every loss spike is your model telling you something. Listen carefully
1hViews 249Likes 1Bookmarks 0
Lunari@0x_lun
@_arohan_ most of the time its just a bad batch and everyone panics anyway
4hViews 220Likes 1
rohan anil@_arohan_
@0x_lun One cannot build an antifragile company without antifragile models
4hViews 165Likes 3
ShitCockaSays@batcz
@_arohan_ telling me I forgot max_grad_norm: 1
3hViews 111
radah@the_radah
@_arohan_ Isn't every loss drop too?
3hViews 108
pushinproto@pushinproto
@_arohan_ That I forgot to clip my gradients? 😛
3hViews 107
Lunari@0x_lun
@_arohan_ stoicism is a fun frame until the spike is a corrupted sample and you wasted three hours debugging your learning rate
4hViews 15
betraidx@betraidx
@_arohan_ also: every flat plateau is your model agreeing with your overfitting
3hViews 2
Alex YGift@Radipdegen
@_arohan_ sometimes the model is just screaming noise into the void tho
how do u tell the difference?
4hViews 2