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.
arXiv:2609.37119v1 Announce Type: cross Abstract: Recent approaches to reinforcement learning (RL) post-training for large language models increasingly remove the critic to reduce training instabilit...
arXiv:2610.11854v1 Announce Type: cross Abstract: Reinforcement learning (RL) methods such as GRPO substantially improve large language model reasoning but often suffer from policy entropy collapse:...
Agentic reinforcement learning (RL) has become a critical stage in the post-training of large language models. Existing critic-free, group-relative methods estimate policy advantages from multiple rollouts, avoiding the substantial memory overhead of conventional proximal policy optimization (PPO) and achieving strong performance on long-horizon interactive tasks.
arXiv:2609.24174v1 Announce Type: new Abstract: Long-horizon language agents receive sparse terminal feedback, while intermediate rubrics provide structured but potentially misspecified assessments o...
arXiv:2602. 21492v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has become a central post-training paradigm for large language models (LLMs), but its performance is highly sensitive to the quality of training problems.
arXiv:2609.36802v1 Announce Type: new Abstract: A key strength of Proximal Policy Optimization (PPO) is its learned critic, which uses historical trajectories collected during reinforcement learning...
arXiv:2602. 04879v3 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has become a cornerstone for fine-tuning Large Language Models (LLMs), with Proximal Policy Optimization (PPO) serving as the de facto standard algorithm.
The paper introduces POISE, a reinforcement learning algorithm that uses a model’s internal states as a value estimator to reduce variance in reinforcement learning with verifiable rewards (RLVR). By employing a lightweight probe that reads internal signals during the forward pass, POISE predicts baselines online and uses a cross‑rollout construction to keep gradients unbiased. Experiments on Qwen3‑4B and OLMo3‑7B‑Instruct‑DPO across six domains show POISE outperforms existing RLVR baselines, offering more stable training and a value model that generalizes across tasks and scales with the policy.
arXiv:2608. 19842v1 Announce Type: new Abstract: Agentic reinforcement learning (RL) has become a critical stage in the post-training of large language models.
arXiv:2607. 04713v1 Announce Type: cross Abstract: Reinforcement learning holds significant potential for training large language models (LLMs) to handle multi-turn interactive tasks.
The paper introduces Entropy‑Normalized Trust Region (ENTR), a method for asynchronous reinforcement learning that adjusts off‑policy ratio thresholds based on token entropy rather than a single magnitude cut‑off. By recognizing that the natural scale of the ratio is set by entropy, ENTR preserves genuine exploration while filtering out noise from low‑entropy, stale data. Experiments on long‑horizon agentic tasks and mathematical reasoning benchmarks show ENTR outperforms existing asynchronous methods, improving BrowseComp‑Plus performance by 6.9 % and enabling stable training up to 30 policy versions of staleness while matching synchronous GRPO at a 2.6× speedup.
arXiv:2606. 01281v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has emerged as a powerful paradigm for enhancing the reasoning capabilities of large language models (LLMs).