Large language model (LLM) agents repeatedly load reusable content, such as skills, documents, and memory entries, into the current context. Re-encoding this content for every request wastes computation. Position-independent caching (PIC) alleviates this by encoding each artifact independently and reusing its key-value...
Xing-Hao Chen, Jun-Nan Dong, Cai Ke et al.· 0 citations
On-policy distillation (OPD) supervises student-generated trajectories with token-level teacher signals. Its sampled-token variant avoids the cost of full-vocabulary probabilities. Yet we find that training on fewer tokens can outperform full-token OPD, challenging the intuition that more supervision improves learning....
An-Hao Zhao, Hao-Ran Xin, Jun-Long Tong et al.· 0 citations
Large language model (LLM) agents repeatedly load reusable content, such as skills, documents, and memory entries, into the current context. Re-encoding this content for every request wastes computation. Position-independent caching (PIC) alleviates this by encoding each artifact independently and reusing its key-value...
Xing-Hao Chen, Jun-Nan Dong, Cai Ke et al.· 0 citations
C3, credit assignment by counterfactual continuation, substitutes one message at a decision point and continues the run to the terminal reward, so its credit is unbiased, exact up to Monte Carlo error.
Yan-Jun Chen, Yi-Rong Sun, Han-Lin Wang et al.· 8 citations· ⚡1
Action shaping gains the counterpart of the shaping theorem, a condition for absorption, together with the mechanism behind it and a diagnostic that reads it, together with the mechanism behind it and a diagnostic that reads it.
Yan-Jun Chen, Jing-Han Wang, Xiao-Yu Shen et al.· 0 citations
This paper aims to present a comprehensive overview of this emerging paradigm and establish a systematic taxonomy of latent CoT methods, categorizing them from token-wise horizontal approaches to layer-wise vertical strategies.
