Unverified50% confidenceFactExact time
Reasoning loops refer to a model repeatedly returning to similar lines of thought or expressions when generating an answer, unable to converge on a final answer; this is common in tasks requiring long-chain reasoning and is often truncated and counted as a failure in evaluations because it exceeds the trial-run count or generation length limit.
1
Sources
50%
Confidence
Long-term
Relevance
11/4/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedLooping is a common failure mode when language models generate long answers, in which the model repeatedly outputs the same or highly similar phrases, sentence patterns or reasoning steps and cannot break out on its own.79% similarUnverifiedExisting long-chain reasoning methods typically rely on a single strategy and are trained with outcome-reward reinforcement learning, but long-horizon planning failures occur from time to time, and the model's implicit reasoning plan gradually drifts away from effective strategies.79% similarVerifiedReasoning models perform internal Chain-of-Thought before returning final results, consuming large amounts of tokens and computation time in exchange for deeper understanding and lower error rates.79% similarUnverifiedReasoning models typically return a separate reasoning or thinking field in the response, distinct from the final answer field.78% similarUnverified同样的模型只改几句提示词，回答效果可能天壤之别，提示词设计本质上是持续测试、不断调整、反复优化的循环77% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1135445API
curl https://kongchang.com/api/v1/knowledge/claims/1135445MCP
get_claim(id=1135445)