TL;DR
StepHint introduces multi-level stepwise hints in reinforcement learning to improve reasoning in large language models by addressing reward sparsity and exploration issues, leading to better performance and generalization.
Contribution
The paper proposes StepHint, a novel RLVR algorithm that uses adaptive multi-level hints from stronger models to enhance exploration and reasoning in LLMs.
Findings
Outperforms existing methods on six mathematical benchmarks.
Demonstrates superior generalization and out-of-domain performance.
Mitigates near-miss reward problem and exploration stagnation.
Abstract
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 generates valid reasoning chains from stronger models and partitions these chains into reasoning steps using our proposed adaptive partitioning method. The initial few steps…
Peer Reviews
Decision·ICLR 2026 Conference Withdrawn Submission
1. The clarity and presentation of the paper is quite clear. The paper makes for an easy read, and the core set of contributions are clearly explained and motivated which I appreciated. 2. The core set of results in Table 1 cover both in-domain and out-of-domain performance which is a nice addition. This improves the empirical significance of the results. 3. The idea of prepending hints from existing reasoning trajectories is well-executed and the authors justify the construction of hints well b
1. I have various questions about the experimental setup, see the questions section for details. I'm willing to raises my score if these questions are addressed. Right now, I view the clarity of the empirical experiments as a weakness. 2. In addition, I have concerns on the significance of the empirical results. I'd like to see a more thorough comparison with baselines that are equally privileged (access to teacher reasoning chains and can RL). See the questions sections for specifics. 3. I'm a
* Elegant method for determining/discretizing natural language reasoning chains into discrete logical steps with the probability of </think> token. * Broad study of the space given these logical steps, e.g. study with partial advantage and training with partial (hinted) trajectories to showcase potential applications of the discretization effort. * Effective in-domain and OOD performance on mathematical and other reasoning datasets for 7B-sized models.
* The new GRPO advantage still relies on exact token match for partial rewards, which is a limitation. One way around this, for example, would be to have a separate judge/verifier decide whether two steps are logically equivalent. Otherwise, I don't think this fully solves the near-miss issue, rather it feels more like a "first step" or a bandage over it. * One of the core contributions is that the automated method for step detection is "good." Besides the limitation that it still relies on hype
The paper identifies two concrete issues in RLVR and provides a structured solution that is easy to follow. The multi-level hint framework conceptually bridges imitation learning and reinforcement learning.
1. The main idea of progressively providing structured hints or intermediate supervision is conceptually similar to **BREAD[1] and curriculum learning algorithms.** The paper fails to discuss how StepHint differs from or improves upon those approaches, which significantly weakens the **novelty claim**. 2. The use of the *probability of generating `</think>`* as the signal for partitioning reasoning steps is somewhat ad hoc. The intuition is weakly justified, and the paper lacks qualitative evide
- The method is benchmarked against a wide array of strong baselines, including vanilla GRPO, SFT, and other RLVR-enhanced models (e.g., ORZ, Oat, LUFFY) . StepHint achieves state-of-the-art results across six in-domain math benchmarks. - The proposed solution is intuitive and directly targets LLM reasoning issues: providing partial expert hints (the 'hints') reduces the search space to mitigate near-misses, while exposing the model to high-quality reasoning paths (the 'multi-level' expert traj
- The paper's central weakness is its framing. The method, which relies on generating expert trajectories and forcing the model to imitate them (either partially or fully via the reference trajectory), appears to be a sophisticated form of curriculum-based SFT or knowledge distillation rather than a novel RL exploration algorithm. - A core technical contribution, the probabilistic partitioning heuristic ($p(</think>|G_i) > p(</think>|G_{i+1})$) in Sec 3.2.1, is not sufficiently justified 18. The
+ The method is evaluated on both math benchmarks and other domains, such as science. + The method is well-motivated. + Experimental results on several benchmarks show the effectiveness of the method.
+ The diversity of the stronger response generators is a concern. Currently, three models are used for reasoning chain generation: DAPO-Qwen-32B, QWQ-32B, and DeepSeek-R1-Distill-Qwen-32B. The authors should consider using other sizes and models beyond the Qwen series. + Pass@k is used to show the method's exploration ability. However, the results are only shown on AIME 24/25, which contain only 60 instances. This is insufficient to demonstrate the exploration ability. Please add results on more
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