arXiv Machine Learning August 4, 2026 By Melveena Jolly, Midhun Xavier

Stabilized Best-of-$K$ Training for Neural Combinatorial Optimization

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arXiv:2608. 00296v1 Announce Type: new Abstract: Leader Reward modifies POMO training to emphasize the best trajectory produced by repeated inference.

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Baseline-Free Policy Optimization for Neural Combinatorial Optimization

arXiv:2606. 10321v1 Announce Type: cross Abstract: Neural combinatorial optimization (NCO) trains autoregressive policies to solve routing problems.

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llms reinforcement-learning benchmarks safety

arXiv Machine Learning

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Rank-Conditioned Sample Reuse for the Plackett--Luce Best-of-$K$ Objective

arXiv:2607. 11146v1 Announce Type: new Abstract: We study the coupled objective J_K^WOR = E_{S ~ PL-WOR_K}[max_{i in S} R_i]: the expected maximum reward of a size-K Plackett-Luce draw without replacement, the law of Gumbel-Top-K / Stochastic Beam Search decoding.

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Less Data, Better Alignment: Data-Centric Multi-Evaluator Agreement for Preference Optimization

arXiv:2607. 25136v1 Announce Type: new Abstract: Research on preference optimization often varies the training objective while holding the data fixed.

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CODS: Iterative Bellman-Residual Data Selection for Reusable Offline Reinforcement Learning

arXiv:2608. 07719v1 Announce Type: new Abstract: Offline reinforcement learning repeatedly trains policies from a fixed transition pool, making redundant data costly across seeds and hyperparameters, while naive subsampling can remove rare transitions needed for long-horizon credit assignment.

By Ibne Farabi Shihab, Sanjeda Akter, Abu Sa-Adat Mohamed Moon-Im Al Ahsan, Md Najmus Swaqeeb, Anuj Sharma

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On Advantage Estimates for Max@K Policy Gradients

arXiv:2606. 06080v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards is widely used for post-training reasoning models, but sparse outcome rewards make exploration difficult.

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When Is an LLM Worth It for Hyperparameter Optimization? A Budget-Matched Study on Tabular Data Finds the Warm-Start Is a Default Configuration, Not the Model

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By Carson Rodrigues, Oysturn Vas, Isaiah Abner DCosta, Nithish Kumar Prabhakaran

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Baseline-Free Policy Optimization for Neural Combinatorial Optimization

Rank-Conditioned Sample Reuse for the Plackett--Luce Best-of-$K$ Objective

Less Data, Better Alignment: Data-Centric Multi-Evaluator Agreement for Preference Optimization

CODS: Iterative Bellman-Residual Data Selection for Reusable Offline Reinforcement Learning

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When Is an LLM Worth It for Hyperparameter Optimization? A Budget-Matched Study on Tabular Data Finds the Warm-Start Is a Default Configuration, Not the Model