Unverified50% confidenceFactExact time
The plug-and-play solution provided by FADE requires no modification to existing RLVR pipelines and yields more stable training signals solely by improving the scheduler.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
11/3/2026
First Seen
Valid until: 2/1/2027
Sources
Related Entities
Related Claims
UnverifiedThe post-training pipeline covers supervised fine-tuning and reinforcement learning, with the reinforcement learning portion using both RLVR and RLHF.77% similarUnverifiedThe generalization of RLVR cannot rely solely on expanding the training distribution; the stability of reasoning strategies requires dedicated design.73% similarUnverifiedCompared to reinforcement learning from human feedback (RLHF), RLVR does not rely on human annotators, has extremely low marginal cost, and can be run at a scale far exceeding that of RLHF.71% similarUnverified通过RLHF、DPO等轻量级对齐技术可实现快速迭代，无需每次都从头预训练71% similarUnverified后训练优化包括指令微调、RLHF或DPO策略调整、系统提示词工程改进及工具调用和多模态融合能力强化71% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1130323API
curl https://kongchang.com/api/v1/knowledge/claims/1130323MCP
get_claim(id=1130323)