arXiv:2606. 10709v1 Announce Type: cross Abstract: The use of GRPO-style algorithms has become the standard strategy for training LLM search agents under outcome-only rewards.
arXiv:2606. 08854v1 Announce Type: cross Abstract: Standard Reinforcement Learning with Verifiable Rewards (RLVR) training allocates a fixed rollout budget to every query, without regard for what each query's difficulty means for the current policy.
arXiv:2605. 11611v3 Announce Type: replace Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a promising paradigm for training agentic retrieval-augmented generation (RAG) systems from outcome-only supervision.
arXiv:2608.23311v1 Announce Type: new Abstract: Policy optimization (PO) for Large Language Models faces a stability--exploration trade-off, currently mediated by an action-side Policy-KL regularizer...
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 AnySearch, a reinforcement‑learning framework that trains a single policy to perform budget‑aware search for large language models under any budget constraint. The training proceeds in two phases: first, the agent learns with explicit budget state injection and structured reasoning prompts under linearly decaying budgets; second, the scaffold is removed and the agent adapts to randomly sampled budgets that match deployment conditions. The reward combines answer accuracy and budget efficiency, with adaptive weighting to emphasize efficiency for high‑accuracy queries and reduce it for low‑accuracy ones. Experiments on seven QA benchmarks demonstrate that AnySearch outperforms baselines across all budget scales, generalizes to unseen constraints, and improves tool productivity without excessive token overhead.
Policy optimization (PO) for Large Language Models faces a stability--exploration trade-off, currently mediated by an action-side Policy-KL regularizer. This puts practitioners in a double bind: keepi...
arXiv:2610.19789v1 Announce Type: cross Abstract: Self-distillation in agentic reinforcement learning has produced a growing number of methods. However, existing evaluations often reuse published bas...
arXiv:2610.15888v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards spends much of its rollout budget on states that yield no learning signal. We recast this as a questio...
Reinforcement learning (RL) is becoming increasingly important for post-training large language models (LLMs). Previous RL pipelines for LLMs were mostly synchronous and batch-interleaved, which is inefficient for long-horizon agentic tasks.
arXiv:2601. 15141v2 Announce Type: replace Abstract: Agentic Reinforcement Learning (RL) has empowered Large Language Models (LLMs) to utilize tools like Python interpreters for complex problem-solving.
arXiv:2607. 07508v1 Announce Type: cross Abstract: Reinforcement learning (RL) is becoming increasingly important for post-training large language models (LLMs).
The paper introduces Conformalized Agentic Search (CAS), a framework that applies Conformal Prediction to improve the reliability of search agents during reinforcement learning fine-tuning. CAS uses an Adaptive Prediction Set (APS) to dynamically truncate retrieved documents based on statistical coverage, and Adaptive Conformal Inference (ACI) to construct confidence-aware prediction sets that penalize low‑confidence trajectories in the Group Relative Policy Optimization objective. Experiments on single‑hop and multi‑hop QA datasets show that CAS enhances reasoning accuracy and reduces redundant tool invocations, offering a more reliable and efficient agent paradigm.