Unverified50% confidenceFactExact time
Perplexity has invested noticeably in self-developed reasoning models in recent years, while NVIDIA has continued to build out its training infrastructure and model research.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
10/22/2026
First Seen
Valid until: 1/20/2027
Sources
Related Entities
Related Claims
UnverifiedResearch teams from Perplexity and NVIDIA jointly proposed a post-training method called JOLT, which lets the same model play both the teacher and student roles and trains it with joint reinforcement learning.69% similarUnverifiedA model's core parameter weights are fixed after training is complete; even when new information is obtained through external tools, the model still relies on the worldview formed during training when making integrated judgments.67% similarUnverifiedThe researchers believe the issue is most likely a systemic byproduct of current mainstream reinforcement learning training paradigms (such as RLHF and RLEF), rather than an engineering error on the part of any individual vendor.67% similarUnverifiedInference-side infrastructure is receiving the same level of attention as training-side infrastructure.67% similarUnverified多头自注意力机制突破了RNN架构只能顺序处理的瓶颈,使训练可高度并行化,支撑千亿乃至万亿参数规模扩展66% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1084765API
curl https://kongchang.com/api/v1/knowledge/claims/1084765MCP
get_claim(id=1084765)