Beyond Reasoning Gains: Mitigating General Capabilities Forgetting in
  Large Reasoning Models
Authors: name: Hoang Phan, name: Xianjun Yang, name: Kevin Yao, name: Jingyu Zhang, name: Shengjie Bi, name: Xiaocheng Tang, name: Madian Khabsa, name: Lijuan Liu, name: Deren Lei
Organization: AI at Meta

Reinforcement learning with verifiable rewards (RLVR) has delivered
impressive gains in mathematical and multimodal reasoning and has become a
standard post-training paradigm for contemporary language and vision-language
models. However, the RLVR recipe introduces a significant risk of capability
regression, where models forget foundational skills after prolonged training
without employing regularization strategies. We empirically confirm this
concern, observing that open-source reasoning models suffer performance
degradation on core capabilities such as perception and faithfulness. While
imposing regularization terms like KL divergence can help prevent deviation
from the base model, these terms are calculated on the current task, thus they
do not guarantee broader knowledge. Meanwhile, commonly used experience replay
across heterogeneous domains makes it nontrivial to decide how much training
focus each objective should receive. To address this, we propose RECAP-a replay
strategy with dynamic objective reweighting for general knowledge preservation.
Our reweighting mechanism adapts in an online manner using short-horizon
signals of convergence and instability, shifting the post-training focus away
from saturated objectives and toward underperforming or volatile ones. Our
method is end-to-end and readily applicable to existing RLVR pipelines without
training additional models or heavy tuning. Extensive experiments on benchmarks
based on Qwen2.5-VL-3B and Qwen2.5-VL-7B demonstrate the effectiveness of our
method, which not only preserves general capabilities but also improves
reasoning by enabling more flexible trade-offs among in-task rewards.