Unverified50% confidenceOpinionExact time
33.5T tokens is at the higher end among current leading models, and the training process requires a stable data supply pipeline, deduplication and quality filtering mechanisms, and the ability to monitor and recover from training instability.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
11/5/2026
First Seen
Valid until: 2/3/2027
Sources
Related Entities
Related Claims
Unverified适当缩短回放池长度或使用优先经验回放、定期检查Q值量级,是维持SAC长时间训练稳定性的工程手段66% similarUnverifiedA typical post-training pipeline is: pre-training → supervised fine-tuning (SFT) → preference optimization (RLHF / DPO / GRPO).66% similarUnverifiedThe key to a high-quality LoRA lies in a closed-loop process: data quality determines the ceiling, training configuration determines whether the ceiling can be approached, and evaluation and iteration determine whether the final output is stable and reproducible.65% similarUnverifiedLarge model training involves multiple stages including data cleaning, annotation, alignment, and safety fine-tuning65% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1141344API
curl https://kongchang.com/api/v1/knowledge/claims/1141344MCP
get_claim(id=1141344)