AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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A series of models are explored that connect item Semantic IDs with natural language in a unified representation space and seek to unlock the potential of natural-language chain-of-thought (CoT) reasoning for recommendation, including OneReason.
During reinforcement learning with verifiable rewards (RLVR), large language models (LLMs) can exploit loopholes in their environments to obtain high rewards without improving the intended capabilities, i.e., reward hacking. Despite its risks to training efficiency and safety, monitoring and mitigating reward hacking d...
CHERRL is introduced, a Controllable Hacking Environment for Rubric-based RL that enables stable reproduction of reward hacking, explicit observation of reward divergence, and identification of hacking onset and analyzes different judge biases from the perspectives of discoverability and exploitability.
RuVerBench is introduced, the first benchmark for assessing LaaJ reliability in rubric verification for agentic scenarios, and the impact of key LaaJ strategies, including prompt design, batching, and majority voting, on rubric verification is analyzed.
TrajDebug is proposed, an error-lifecycle tracing framework that addresses long-trajectory error discovery with multi-granularity history compression and evidence-based error identification, and supports critical attribution by tracing each error's resolution status and terminal impact.
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