Foundation-Sec-8B-Reasoning Accuracy Under RLVR Across Programming Languages in Big-Vul

Abstract

Abstract: Visual question answering increasingly requires multi-step reasoning. Recent post-training with reinforcement learning under verifiable rewards (RLVR) and Group Relative Policy Optimization (GRPO) can improve multimodal reasoning, but most approaches rely on sparse outcome-only rewards. As a result, they struggle to tell whether an incorrect answer comes from a small mistake late in the reasoning or from an unhelpful trajectory from the start. A common solution is to train a process reward model (PRM) for step-level supervision, but this typically requires large-scale high-quality chain-of-tho

Research Question

What is the impact of reinforcement learning from verifiable rewards (RLVR) on the accuracy of Foundation-Sec-8B-Reasoning in reasoning-based security tasks across different programming languages in the Big-Vul benchmark?

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