Balancing Reference Guidance and Free Generation in Trajectory Rollouts for Reasoning RL
Quick Answer
This paper shows that The Adaptive Reference Guidance (ARG) method enhances trajectory generation in reasoning models like Qwen3-4B and Qwen3-8B, achieving the highest pass@12 rates across five benchmarks in Group Relative Policy Optimization.
Quick Take
By balancing reference guidance and free generation, ARG constructs correct trajectories efficiently without estimating success probabilities.
Key Points
- ARG constructs correct trajectories within a fixed generation budget.
- Achieved highest aggregate pass@12 rates among evaluated methods.
- Utilizes prefix continuation to balance reference guidance and free generation.
- Applies to all-failure groups in (GRPO).
- Derives KL divergence to optimize the amount of reference guidance.
DeepSignal Analysis
What happened
The Adaptive Reference Guidance (ARG) method was introduced to improve trajectory generation in reasoning models, specifically Qwen3-4B and Qwen3-8B. ARG balances reference guidance with free generation, allowing models to construct correct trajectories efficiently. It achieved the highest pass@12 rates across five benchmarks in Group Relative Policy Optimization.
Key evidence
- ARG enhances trajectory generation in reasoning models like Qwen3-4B and Qwen3-8B, achieving the highest pass@12 rates across five benchmarks.
- The method constructs correct trajectories without estimating success probabilities, relying instead on prefix continuation from a verified reference solution.
- Experiments demonstrated that ARG outperformed other methods in aggregate pass@12 while maintaining competitive average sampled accuracy.
Why it matters
The development of ARG is significant as it addresses the challenge of balancing guidance and autonomy in AI reasoning models. By optimizing trajectory generation without the need for success probability estimation, ARG could streamline training processes and improve model performance in complex reasoning tasks. This advancement may influence future research and applications in artificial intelligence, particularly in areas requiring robust decision-making capabilities.
Paper Resources
📖 Reader Mode
~2 min readAbstract:A verified reference solution provides a correct trajectory for training a reasoning model. Alternatively, a prefix of the reference can guide the model in generating a trajectory of its own. How much reference guidance should we provide? We study this question through prefix continuation, where the model continues from a reference prefix and keeps the resulting trajectory if it passes verification, falling back to the reference otherwise. Since both procedures produce correct trajectories, we compare their distributions with the ideal distribution, the model's own distribution conditioned on successful verification. For one continuation, we derive the KL divergence in closed form, which, up to a bounded term, decreases with the product of the probability of generating a different correct trajectory and the reference surprisal, the negative log probability of the reference suffix given the prefix. Since a longer prefix tends to raise the former but lowers the latter, continuation success alone does not determine the preferred amount of guidance. From this analysis, we learn a prefix selector shared across training questions from continuation outcomes, without estimating success probabilities or additional generation. The resulting Adaptive Reference Guidance (ARG) constructs correct trajectories within a fixed generation budget, and we apply it to all-failure groups in Group Relative Policy Optimization (GRPO). Experiments on Qwen3-4B and Qwen3-8B across five mathematical reasoning benchmarks show that ARG achieves the highest aggregate pass@12 among the evaluated methods with competitive average sampled accuracy.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.11128 [cs.AI] |
| (or arXiv:2610.11128v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.11128 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Hanyu Wang [view email]
[v1]
Thu, 8 Oct 2026 02:54:48 UTC (411 KB)
— Originally published at arxiv.org
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