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LLMZero: Discovering Adaptive Training Strategies for RL Post-Training via LLM Agents

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arXiv:2606. 18388v1 Announce Type: new Abstract: RL post-training strategies are dataset-dependent and reveal a recurring empirical pattern: capacity parameters accumulate monotonically across stages, while regularization parameters predominantly oscillate in response to shifting training dynamics.

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Authors: Haoyang Fang, Wei Zhu, Boran Han, Alex Zhang, Zhenyu Pan, Shuo Yang, Shuai Zhang, Jiading Gai, Peng Tang, Cuixiong Hu, Xuan Zhu, Huzefa Rangwala, George Karypis, Bernie Wang
Released: 2026-06-18 (1mo ago)
Topic: Related to current project
Relation to my work: Read next because LLMZero: Discovering Adaptive Training Strategies for RL Post-Training via LLM Agents overlaps with clean result "The marker is a representational handle, not a behavioural one — sharing it between a villain persona and the assistant transfers no misalignment (HIGH confidence)", experiment "#351 follow-up: broader-vocab position-0 sweep at T=1.0 + position-1 suffix isolation", experiment "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: rate, stage, lora, model. Source: arxiv cs.LG (Machine Learning).
arXiv: 2606.18388

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