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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.
Grounded Transitive RL (GTRL) is an offline goal‑conditioned reinforcement learning algorithm that improves upon divide‑and‑conquer value learning by grounding updates with a one‑step temporal‑difference (TD) target. By adding this TD target rather than replacing it, GTRL ensures every state‑goal pair receives an update and corrects bias from hindsight relabeling through reweighting based on reachability. The method was evaluated on nineteen OGBench tasks across stochastic, deterministic, and stitching environments, achieving the highest average success rate among compared approaches.
arXiv:2605. 17877v2 Announce Type: replace Abstract: A significant hurdle for current LLMs is the execution of complex, multi-stage tasks.
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.
arXiv:2609.36178v1 Announce Type: cross Abstract: Group Relative Policy Optimization (GRPO) has become a promising approach for training large language model agents. However, its uniform assignment o...
arXiv:2606. 30852v1 Announce Type: new Abstract: Reasoning models spend different amounts of useful computation across instances, but it remains unclear when a learned stopping rule improves over simple confidence or convergence thresholds.