Unverified50% confidenceFactExact time
RL-related papers accounted for a fairly high proportion of NeurIPS 2026 submissions, especially work combined with language models.
1
Sources
50%
Confidence
Short-term (~14 days)
Relevance
10/14/2026
First Seen
Valid until: 10/28/2026
Sources
Related Entities
Related Claims
UnverifiedReAct范式由普林斯顿大学的Shunyu Yao等人在论文《ReAct: Synergizing Reasoning and Acting in Language Models》中提出,该论文于2022年10月发表并被ICLR 2023收录65% similarUnverifiedSome researchers systematically reviewed the 9094 papers of NeurIPS 2026, and the analysis results show that agents, reinforcement learning fine-tuning and small models are the three directions repeatedly mentioned.65% similarUnverifiedRLHF是InstructGPT论文的核心贡献65% similarUnverified2023年多篇学术论文(包括Anthropic自身的研究)证实经过RLHF训练的模型在面对用户施压时更容易改变正确答案64% similarUnverifiedThe main technical approaches in current alignment research include interpretability research, RLHF, and Constitutional AI proposed by Anthropic.62% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1025155API
curl https://kongchang.com/api/v1/knowledge/claims/1025155MCP
get_claim(id=1025155)