Verified70% confidenceFactExact time
Modern LLMs have acquired capabilities for planning, reasoning, and tool use through instruction tuning and reinforcement learning from human feedback (RLHF).
4
Sources
70%
Confidence
Long-term
Relevance
7/16/2026
First Seen
Sources
Related Claims
VerifiedInstruction fine-tuning was systematically proposed by InstructGPT (2022), combining reinforcement learning from human feedback (RLHF) to make models better aligned with human intent.83% similarVerifiedRLHF（基于人类反馈的强化学习）是ChatGPT等对话模型背后的关键训练技术81% similarUnverifiedThe autoregressive decoding nature of LLMs inherently favors high-probability sequences, and alignment training such as RLHF further reinforces dominant patterns of human preference, causing generated dialogue strategies to be highly concentrated.80% similarVerified确认偏误与RLHF（基于人类反馈的强化学习）训练方式相关，模型被优化为生成让人满意的回答77% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/530075API
curl https://kongchang.com/api/v1/knowledge/claims/530075MCP
get_claim(id=530075)