tokentideAI/SDRL-Qwen3-32B
AI model by tokentideAI
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LLMs as Adaptive Meta-Solvers: Strategy-Diverse RL for Industrial-Scale Optimization We introduce Strategy-Diverse Reinforcement Learning (SDRL), a framework that train open-source LLMs into adaptive optimization meta-solvers for real-world, industrial-scale tasks. Instead of committing to a single solver-integrated paradigm, the model adaptively routes each problem to the most appropriate computational strategy: SDRL leverages the empirical complementarity of these three strategy families through a correctness-gated hierarchical diversity reward that promotes exploration both across strategies and within each strategy, preventing premature strategy collapse. A mixed-format training scheme jointly supports self-contained textual problems and file-grounded industrial instances whose data is distributed across external files. We evaluate on seven NL-to-Opt benchmarks: NL4Opt, MAMO-EasyLP and MAMO-ComplexLP, IndustryOR, OptMATH-Bench, OptiBench, and MIPLIB-NL (Li et al., 2026). The first six use self-contained textual inputs, while MIPLIB-NL introduces file-grounded, industrial-scale optimization tasks that demand the ability to dynamically read and process external data files at runtime. Following the rigorous evaluation…
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tokentideAI/SDRL-Qwen3-32B is a task-unspecified model repository published by tokentideAI on Hugging Face. A library was not reported.
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- 2026-10-08T06:18:17.000Z
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- 2026-10-08T06:18:17.000Z
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