To Reason or to Fabricate: Reasoning Without Shortcuts via Hint-Anchored Pairwise Aggregation
arXiv:2606.29481v1 Announce Type: cross Abstract: While reinforcement learning (RL) significantly enhances LLM reasoning, its efficacy is severely undermined by Pre-RL data overlap, where RL datasets overlap with pretraining or SFT corpora, causing models to exploit shortcuts by memorizing correct answers and fabricating post-hoc reasoning. To address this, we introduce HIPPO, a novel RL framework that integrates hint-injected aggregation with a tailored pairwise reward model. By utilizing hint injection to deliberately trigger overlap-induced behaviors, the resulting traces naturally serve as
The increasing sophistication and widespread deployment of LLMs highlight the urgent need to address reliability and prevent 'shortcut' reasoning, especially in critical applications.
Improving the robustness and trustworthiness of LLM reasoning without memorization is crucial for their adoption in high-stakes environments and for advancing true artificial intelligence.
This research introduces a novel reinforcement learning framework, HIPPO, directly addressing the critical issue of data overlap in LLM training, which could lead to more reliable and less 'fabricating' AI models.
- · AI developers
- · LLM application providers
- · Users of AI systems
- · Models reliant on simple data memorization
- · Uncritically deployed LLMs
More sophisticated and less fallible LLMs will emerge, increasing trust in AI-generated outputs.
The demand for high-quality, non-overlapping datasets for RL will increase, driving new data curation strategies.
This could accelerate the integration of AI into complex decision-making processes where 'fabrication' is unacceptable.
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Read at arXiv cs.AI