Beyond Reasoning Gains: Mitigating General-Capability Forgetting in Large Reasoning Models

The Hidden Cost of Reasoning Gains

A new arXiv paper (2510.21978v2) tackles a critical and often overlooked problem in large language model development: when you train a model to become better at reasoning, it tends to forget other general capabilities. The research focuses on reinforcement learning with verifiable rewards (RLVR), a technique that has driven impressive improvements in mathematical and multimodal reasoning for both language and vision-language models.

The core finding is that RLVR, while powerful for specialized reasoning tasks, introduces a trade-off. As models are optimized to solve complex logic problems, they lose proficiency in broader skills like factual recall, instruction following, and creative generation. This phenomenon—general-capability forgetting—mirrors the catastrophic forgetting seen in continual learning, but here it occurs within a single post-training phase.

Why This Matters

This research arrives at a pivotal moment. RLVR has become the default post-training paradigm for cutting-edge models, powering everything from advanced math solvers to multimodal assistants. The assumption has been that reasoning gains are pure upside. This paper challenges that assumption by quantifying the downside.

For AI practitioners, the implications are immediate. If you are fine-tuning a model for a specific reasoning task—say, code generation or scientific problem-solving—you may inadvertently degrade its performance on tasks you still need. A model that becomes a math genius might simultaneously become worse at summarization, translation, or following nuanced user instructions. The paper suggests that without mitigation strategies, the very process that makes models smarter in one dimension makes them dumber in others.

Implications for AI Practitioners

First, evaluation must expand. Standard benchmarks for reasoning tasks are insufficient. Practitioners need to track general capability metrics—factual accuracy, instruction adherence, and fluency—alongside reasoning scores. A model that scores 95% on math but drops from 90% to 70% on general QA is not a net improvement for most applications.

Second, mitigation strategies are essential. The paper implies that naive RLVR training is suboptimal. Practitioners should explore techniques like multi-task reinforcement learning, where the model is rewarded not only for correct reasoning but also for retaining general knowledge. Alternatively, replay buffers or regularization methods that penalize forgetting could be integrated into the training loop.

Third, the choice of verifiable rewards matters. Not all reasoning tasks are equal. If the reward signal is too narrow, forgetting accelerates. Designing reward functions that balance specialization with breadth may be the key to sustainable improvement.

Finally, this is a warning against over-optimization. The AI field has a tendency to chase leaderboard scores. This research reminds us that real-world deployment requires balanced capability, not just peak performance on a single metric.

Key Takeaways

• RLVR training for reasoning causes significant forgetting of general capabilities, including factual recall and instruction following.
• Practitioners must monitor broad capability metrics, not just reasoning benchmarks, during post-training.
• Mitigation techniques like multi-task learning or regularization are necessary to preserve model versatility.
• Over-optimizing for a single reasoning task risks creating a model that is brilliant in one area but degraded in others—a net negative for most applications.