Unverified50% confidenceBenchmarkExact time
When the agent is allowed to invoke post-trained VLA or WAM policies, overall task success rate rises to the level of the strongest comparison policy, while token cost drops significantly.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
10/27/2026
First Seen
Valid until: 1/25/2027
Sources
Related Entities
Related Claims
Unverified分配给预训练的算力越多，下游任务的pass@1奖励就越高，预训练损失越低模型在后续RL中获得的收益越大75% similarUnverifiedThe true cost reductions come from post-training optimization, request routing, and capacity terms negotiated down based on usage volume; the focus should be on the cost per completed task, not the token unit price.73% similarUnverifiedOnly when a certain type of task is sufficiently high-frequency, stable, and cost-sensitive does migrating it from a general-purpose model to a specially trained model potentially realize significant cost benefits.72% similarVerifiedCompared with traditional end-to-end imitation learning, VLA models have stronger generalization capabilities, but high inference latency, stringent requirements for training data diversity, and deployment on real-time control and low-cost hardware remain the primary challenges.72% similar
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