Large Language Model agents augmented with external skill banks have demonstrated remarkable performance on sequential decision-making tasks. However, existing methods update skills in an undirected, unverified manner and prematurely penalize agents for failing to utilize immature skills. We introduce a structured cognitive representation that decomposes both general and task-specific skills into four dimensions—D-PLAN, D-TRACK, D-EXEC, and D-GUARD—enabling precise failure attribution. Building upon this representation, we propose A²SE, an ability-aligned skill evolution framework that synchronizes skill bank updates with the agent’s learning trajectory. Specifically, A²SE concentrates evolution on the identified bottleneck dimension, safeguards updates via a causal verification mechanism with per-task-type rollback, and employs a competence-aligned skill reward to alleviate premature penalization. Extensive experiments on ALFWorld and WebShop demonstrate that A²SE achieves 11.0% and 7.1% improvements over the strongest baselines, respectively, with ablation studies confirming the necessity of each proposed component.

Reinforcement Learning with Verifiable Rewards (RLVR) frequently suffers from mode collapse due to the inherent sparsity of feedback signals. While strategies such as entropy regularization introduce randomness, they lack directionality. Simply incorporating diversity rewards is overly one-sided and fails to identify potential logical errors or hallucinations. To address these limitations, we propose VANE (Value-Aligned Novelty Exploration), a method that simultaneously quantifies novelty across the outcome space (via reward or solution divergence) and the semantic process space (via semantic process divergence). Moreover, VANE employs a value-alignment mechanism that symmetrically amplifies scarce, high-quality solutions while explicitly penalizing diverse yet erroneous reasoning paths. Extensive experiments on models such as Qwen2.5-Math-7B across eight benchmarks—encompassing both large-scale mathematical reasoning and out-of-distribution (OOD) tasks—demonstrate the effectiveness and generalization of the proposed method.

Long-horizon LLM agents can benefit from reusable skills, yet existing skill-based methods often rely on external skill generators during training or persistent skill retrieval at inference, increasing engineering complexity, context length, and deployment latency. We propose Self-Internalizing Reinforcement learning with Intrinsic skills (SIRI), a three-phase framework that enables agents to discover, validate, and internalize skills without external skill generators or inference-time skill banks. SIRI first warms up the policy with GiGPO to acquire basic interaction ability and collect successful skill-free trajectories. It then performs self-skill mining, where the current policy summarizes compact skills from its own successful plain rollouts and validates them through paired skill-augmented and skill-free rollouts. Finally, SIRI distills only beneficial skill-guided action tokens into the plain policy using trajectory-level utility and action-level advantage. At inference, the agent runs with the original prompt only. On ALFWorld and WebShop with Qwen2.5-7B-Instruct, SIRI improves GiGPO from 0.908 to 0.930 on ALFWorld and from 0.728 to 0.813 on WebShop, outperforming prompt-based, RL-based, and memory-augmented baselines. Further analysis shows that our self-mining strategy can achieve performance comparable to distillation with closed-source large model.

Reinforcement Learning with Verifiable Rewards (RLVR) has become the standard paradigm for enhancing reasoning capabilities in Large Language Models, yet on-policy algorithms like GRPO suffer from sample inefficiency. Current experience replay methods for RLVR typically replay correct trajectories to consolidate learned reasoning patterns and accelerate convergence, but overlook the vast failure space. This work investigates how to effectively replay failure trajectories. We find that the high heterogeneity of failures renders random replay ineffective, and that high-value negatives should be both gradient-efficient and structurally proximal to correct solutions. To this end, we propose NexGRPO, which employs mid-confidence gating to filter invalid noise and saturated errors, and utilizes boundary failure sampling to retrieve boundary errors semantically similar to correct solutions for targeted refinement. Extensive experiments on mathematical and general reasoning benchmarks demonstrate that NexGRPO outperforms strong baaselines and achieves improved out-of-distribution generalization.