arXiv:2605. 27701v2 Announce Type: replace Abstract: We present Frost Training, a method for improving Monte Carlo-based policy optimization for a large family of LLM-as-a-judge tasks called Cross-Entropy Games.
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arXiv:2605.26958v2 Announce Type: replace-cross Abstract: Reinforcement learning in open-ended long-form generation is challenging because reliable reference answers and automatic metrics are often u...
The paper introduces FastRL, a reinforcement learning framework designed to enhance the efficiency of Group Relative Policy Optimization (GRPO) and its variants. FastRL employs an advantage-aware pruning strategy that retains high-advantage trajectories while preserving gradient diversity, and an adaptive rollout sampling mechanism that adjusts sampling scale during training based on historical pruning data. Experiments show that FastRL can be integrated into GRPO, DAPO, and GSPO, yielding a 2.07× speedup on Geometry3K and GeoQA8K-R1V and a 1.64% accuracy improvement on visual reasoning benchmarks.
arXiv:2609.39757v1 Announce Type: cross Abstract: Black-box distillation is a practical route for transferring capabilities from API-accessible large language models that expose only text outputs int...
DRACO introduces a method for fine‑grained credit assignment in long‑horizon reinforcement learning tasks that lack verifiable rewards. It dynamically generates multi‑criteria rubrics during training, scores them once per trajectory, and redistributes the resulting judgment over the steps responsible for each rubric to produce differentiated per‑step advantages. Experiments on AppWorld and Tau‑Bench show that DRACO outperforms baseline models and other rubric‑based approaches, achieving significant performance gains without relying on verifiers.
arXiv:2608. 11669v1 Announce Type: cross Abstract: Reinforcement learning against rubrics, lists of criteria graded by an LLM judge, has become a standard way to post-train language models on tasks with no deterministic answer.
The paper introduces Follow the Winners (FTW), a critic‑free policy‑learning algorithm that adapts the cross‑entropy method for reinforcement fine‑tuning of agentic large language models. FTW replaces group rollouts with an ordinal filter on replay‑buffer samples, achieving polynomial concentration in the order statistic of returns and offering a bounded risk‑seeking offset that balances variance reduction. Experiments on Sokoban and Search‑R1 show that FTW matches the performance of GRPO and PPO while reducing reliance on value models or repeated rollouts.