Unverified50% confidenceFactExact time
The reasoning capability of diffusion language models can be enhanced through inference-time search strategies without relying on additional post-training.
1
Sources
50%
Confidence
Long-term
Relevance
10/20/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedLarge language models default to optimizing for plausibility rather than discriminability when generating options, because the training objective is to predict plausible words.78% similarUnverified若训练集能充分覆盖推理时可能出现的工具类型和参数组合，小模型同样能学到可靠的格式生成能力，而不必依赖大模型的深层语义推理78% similarUnverifiedLLM-based agentic optimization methods do not rely on predefined search spaces or scheduling templates, but instead leverage the model's code understanding and generation capabilities to explore more open-ended optimization paths.77% similarVerifiedThe reasoning paradigm is essentially the application of reinforcement learning to the post-training stage of language models, allowing the model to autonomously explore reasoning paths and receive reward feedback based on answer correctness, first publicly demonstrated in the o1 series of models.76% similarVerifiedThe effectiveness of prompt engineering is rooted in the large language model's In-Context Learning capability, which enables task switching without modifying model weights.75% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1074012API
curl https://kongchang.com/api/v1/knowledge/claims/1074012MCP
get_claim(id=1074012)