Long-term predictive capability of deep learning models has always been a bottleneck, as errors gradually accumulate along with autoregressive iterations, ultimately deviating significantly from the real data distribution. However, the long-term predictive capability of deep learning is crucial in practical application...
Large language models can gain accuracy by using more computation at inference, most visibly through chain-of-thought, where the model proceeds step by step in language before answering. The mechanism that converts the extra computation into a better answer is what we call reasoning. How reasoning should be designed fo...
Wenhao Deng· Proceedings of the 20th ACM...· 0 citations
It is indicated that retrieval-stage filtering can reduce the candidate action space exposed to agents before execution, complementing downstream tool-use safeguards, and providing a conservative operating point for safety-critical deployments.
Qinfei Li, Xiaoxuan Dong, Jin Zhang et al.· 2 citations
Rec is proposed, an RL-free framework that decouples reasoning from prediction, overcoming the fixed d-dimensional state bottleneck of prior methods, and points to a decoupled, multi-vector recipe that unleashes latent reasoning from the single-state bottleneck of prior methods.
Wenhao Deng, Junchen Fu, Han-Wen Du et al.· arXiv.org· 0 citations
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