A pseudonymous DeepSeek enthusiast with a sizable following is floating that the lab's anticipated V4.1 update could land five to eight times the reinforcement learning compute budget of Zhipu AI's freshly dropped GLM-5.2, pointing to raw hardware headroom as the deciding factor rather than any leaked roadmap.
On priors (signaling about compute and revealed capacity, maturity of the model design…), I think DeepSeek-V4.1 ought to receive 5-8 times more RL compute than GLM-5.2. Pretty much the only way it'd fail to exceed 5.2 is if they're actually worse on algorithms or data.
DeepSeek's known cluster scale supports the idea of heavier post-training runs, yet nothing confirms whether that capacity will be directed at V4.1 specifically or how GLM-5.2's own RL stage compares in practice.
Community chatter keeps circling back to training efficiency and agentic benchmarks, but without disclosed compute figures or side-by-side evaluations the 5-8x claim stays an untested projection for now.
Users praise DeepSeek-V4.1 post-training papers as generational when discussing its predicted need for far more RL compute than GLM-5.2.
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@teortaxesTex don't get me excited
On priors (signaling about compute and revealed capacity, maturity of the model design…), I think DeepSeek-V4.1 ought to receive 5-8 times more RL compute than GLM-5.2. Pretty much the only way it'd fail to exceed 5.2 is if they're actually worse on algorithms or data.
@teortaxesTex V4 wasn’t trained on comparatively much input data right?
@SilentHacks0 V4 and GLM-5 are comparably well trained
@teortaxesTex V4.1 post training papers are going to be generational