Unverified50% confidenceFactExact time
The RLHF process is complex and has high memory usage; it usually requires the policy model, reference model, reward model, and value function model to all be resident at the same time, so its GPU resource requirements are far higher than those of SFT.
1
Sources
50%
Confidence
Long-term
Relevance
10/24/2026
First Seen
Sources
Together AI B200 Training Price Cut: $9/GPU-Hour for the First Eight Weeks
twittertogethercompute10/20/2026
Related Entities
Related Claims
UnverifiedThe computational cost of RLHF is extremely high: it requires simultaneously running a policy model, value model, reward model, and reference model, with memory consumption several times that of supervised learning.76% similarVerifiedRLHF requires simultaneously maintaining four copies—the policy model, reference model, reward model, and value model—resulting in enormous GPU memory consumption and unstable training.75% similarUnverifiedPPO in RLHF scenarios requires simultaneously maintaining four neural networks: the policy model, reference model, reward model, and value model.73% similarUnverifiedRLHF has become the standard process for aligning large models, but its computational cost and stability issues remain prominent.73% similarUnverifiedGPU memory consumption during large model inference comes primarily from three components: model weights, activations, and KV Cache.71% similar
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