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Bolin Ding

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#machine learning Preprint Sep 2026

Fine-Tuning on Self-Generated and Reward-Weighted Data: Learning Dynamics, Convergence Rates, and Benefits of Off-Policyness

A unified theory for RE(S) is developed that covers the full spectrum of S, and can be interpreted as a stage-wise optimization process, where each stage takes $S$ gradient steps for minimizing the Kullback-Leibler distance to a fixed reward-weighted rollout distribution.

Zhi-Wei Wang, Yan-Xi Chen, Ya-Liang Li et al. · 0 citations
Preprint Aug 2026

Private Direct Preference Optimization for LLM Alignment

This paper formalizes preference privacy, a label-DP-style privacy notion for DPO that protects only the relative preference between candidate responses, assuming an adversary who already knows the prompt and responses, and designs PrivDPO, a DPO variant that enforces preference privacy while remaining compatible with...

Yang-Fan Jiang, Fei Wei, Ergute Bao et al. · 0 citations
Preprint Aug 2026

Context as an Environment: Programmatic Context Management for Long-Horizon Agents

LLM agents increasingly take on long-running tasks whose history grows far beyond a single model context window. Existing approaches compress earlier interactions or extract selected information into fixed memory representations, committing to what to preserve before future needs are known. We present Scroll, a context...

Yin Lin, Elaine Ang, E. Zhu et al. · 2 citations
Preprint Jul 2026

From Atomic Actions to Standard Operating Procedures: Iterative Tool Optimization for Self-Evolving LLM Agents

EvoSOP is introduced, a framework that empowers agents to extract SOPs from execution trajectories and iteratively optimize the toolset through a systematic lifecycle of construction, merging, evaluation, and pruning, providing a scalable pathway for the development of self-evolving agents.

Haipeng Ding, Yuexiang Xie, Zhewei Wei et al. · 2 citations

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