Large reasoning models (LRMs) have achieved strong performance enhancement through scaling test time computation, but due to the inherent limitations of the underlying language models, they still have shortcomings in tasks that require precise computation and extensive knowledge reserves. Tool-Integrated Reasoning (TIR) has emerged as a promising paradigm that incorporates tool call and execution within the reasoning trajectory. Although recent works have released some powerful open-source TIR models, our analysis reveals that these models still suffer from critical deficiencies. We find that when the reasoning of the model conflicts with the tool results, the model tends to believe in its own reasoning. And there are cases where the tool results are correct but are ignored by the model, resulting in incorrect answers, which we define as “Tool Ignored”. This indicates that the model does not know when to trust or ignore the tool. To overcome these limitations, We introduce Adaptive Tool Trust Calibration (ATTC), a novel framework that guides the model to adaptively choose to trust or ignore the tool results based on the confidence score of generated code blocks. The experimental results from various open-source TIR models of different sizes and across multiple datasets demonstrate that ATTC effectively reduces the "Tool Ignored" issue, resulting in a performance increase of 4.1% to 7.5%.

Reinforcement learning (RL) has demonstrated considerable promise in enhancing large language models. However, its application to Mixture-of-Experts (MoE) architectures is frequently hindered by training instability, primarily stemming from token-level misalignment in expert assignments between current and behavior policies. Existing approaches often oscillate between overly coarse sequence-level importance sampling, which ignores token-specific discrepancies, and restrictive expert-selection constraints that suppress beneficial policy exploration. To bridge this gap, we propose Expert Relative Policy Optimization (ERPO), which introduces expert-level importance sampling. By grouping tokens according to their routing assignments, ERPO mitigates the high variance of token-level importance sampling while overcoming the token-agnostic limitations of sequence-level methods. Furthermore, ERPO leverages this expert-centric granularity to introduce an Expert-Selection Entropy Reward, which dynamically adjusts routing uncertainty based on task-specific feedback. This unique mechanism ensures a rigorous alignment between reward signals and policy updates—a capability inherently unattainable by traditional importance sampling methods. Experimental results demonstrate that ERPO significantly outperforms strong baselines across multiple reasoning tasks, highlighting the efficacy of tailoring RL objectives to the structural inductive biases of MoE models.

Reinforcement learning with verifiable rewards (RLVR) is a promising approach for improving the complex reasoning abilities of large language models (LLMs). However, current RLVR methods face two significant challenges: the near-miss reward problem, where a small mistake can invalidate an otherwise correct reasoning process, greatly hindering training efficiency; and exploration stagnation, where models tend to focus on solutions within their ”comfort zone”, lacking the motivation to explore potentially more effective alternatives. To address these challenges, we propose StepHint, a novel RLVR algorithm that utilizes multi-level stepwise hints to help models explore the solution space more effectively. StepHint partitions valid reasoning chains into reasoning steps using our proposed adaptive partitioning method. The initial few steps are used as hints, and simultaneously, multiple-level hints (each comprising a different number of steps) are provided to the model. This approach directs the model’s exploration toward a promising solution subspace while preserving its flexibility for independent exploration. By providing hints, StepHint mitigates the near-miss reward problem, thereby improving training efficiency. Additionally, the external reasoning pathways help the model develop better reasoning abilities, enabling it to move beyond its ”comfort zone” and mitigate exploration stagnation. StepHint outperforms competitive RLVR enhancement methods across six mathematical benchmarks and two out-of-domain benchmarks.