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OctoLong is introduced, a context engineering pipeline that instruments an AST parser, a language server backend, and a package manager to facilitate the recursive retrieval of code references, enabling the curation of dependency-rich code contexts of millions of tokens in length.
The results show that improving IF in LRMs can significantly enhance privacy, suggesting a promising direction for future privacy-aware LRMs, and introduces an SFT dataset that teaches models to follow general instructions throughout their reasoning process.
Evidence that parameter-space exploration can improve reinforcement learning for LLMs is presented, and a family of methods called Perturbed Parameter Policy Optimization (3PO) is introduced which use different sampling strategies and different rollout grouping for reward estimation.
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