arXiv:2606. 23937v1 Announce Type: cross Abstract: Exact-match retrieval recall is often used as a proxy for whether a retriever supplies useful policy context to a downstream decision model.
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The paper introduces GIST, a Gibbs‑Targeted Hierarchical Data Selection method for offline reinforcement learning with verifiable rewards (RLVR). By analyzing the bidirectional KL divergence between a policy and its Gibbs‑optimal target, GIST selectively supervises correct responses on‑policy and incorrect responses near the policy’s mode, while focusing token supervision on mismatched tokens. Experiments on GSM8K, MATH‑500, and MBPP show that GIST improves average accuracy across three models and outperforms seven baselines on Qwen3‑0.6B/GSM8K, all while supervising 66.6% fewer tokens.
arXiv:2602. 05459v2 Announce Type: replace Abstract: Offline goal-conditioned reinforcement learning (GCRL) is typically benchmarked by the best tuned success rate of each method.
The paper introduces a denoised counterfactual protocol to accurately measure the selectable headroom of world action models (WAMs) and uses these measurements to build a fail‑gated selector. Across four WAMs and two benchmarks, the study shows that the standard estimate overstates selectable headroom, that structured action perturbations can add up to 16 points of headroom, and that imagined futures retain candidate‑outcome information without loss. The selector improves closed‑loop task success from 76.9 % to 81.6 % on held‑out training episodes and from 62.6 % to 69.8 % on unseen tasks.
arXiv:2605. 17877v2 Announce Type: replace Abstract: A significant hurdle for current LLMs is the execution of complex, multi-stage tasks.
arXiv:2607. 27422v2 Announce Type: replace Abstract: Goal-conditioned action generators can produce many plausible actions.
arXiv:2607. 29606v1 Announce Type: cross Abstract: A fundamental challenge of vector search is achieving consistently high recall while minimizing computational costs.