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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.06850 |
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| _version_ | 1866913099574935552 |
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| author | Zhu, Rui Bai, Weiheng Wu, Qiushi Ren, Yang Tang, Haixu Liu, Yuchu |
| author_facet | Zhu, Rui Bai, Weiheng Wu, Qiushi Ren, Yang Tang, Haixu Liu, Yuchu |
| contents | Reinforcement Learning (RL) has emerged as a crucial paradigm for unlocking the advanced reasoning capabilities of Large Language Models (LLMs), encompassing frameworks like RLHF and RLAIF. Regardless of the specific optimization algorithm (e.g., PPO, GRPO, or Online DPO), online RL inherently requires an exploratory trajectory generation (rollout) phase. However, for long-context reasoning tasks, this rollout phase imposes a severe ``memory wall'' due to the exorbitant Key-Value (KV) cache footprint. While applying KV cache compression during rollouts mitigates this memory overhead, it induces a critical off-policy bias. Although modern KV compression is often nearly lossless during standard inference, even minuscule approximation errors are drastically amplified by the inherent instability of RL optimization. Specifically, the sampler generates responses under a sparse context, whereas the learner updates parameters using the full, dense context. Existing statistical solutions, such as importance reweighting, struggle to correct this magnified bias, suffering from high gradient variance and severe sample inefficiency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_06850 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | How to Compress KV Cache in RL Post-Training? Shadow Mask Distillation for Memory-Efficient Alignment Zhu, Rui Bai, Weiheng Wu, Qiushi Ren, Yang Tang, Haixu Liu, Yuchu Machine Learning Artificial Intelligence Reinforcement Learning (RL) has emerged as a crucial paradigm for unlocking the advanced reasoning capabilities of Large Language Models (LLMs), encompassing frameworks like RLHF and RLAIF. Regardless of the specific optimization algorithm (e.g., PPO, GRPO, or Online DPO), online RL inherently requires an exploratory trajectory generation (rollout) phase. However, for long-context reasoning tasks, this rollout phase imposes a severe ``memory wall'' due to the exorbitant Key-Value (KV) cache footprint. While applying KV cache compression during rollouts mitigates this memory overhead, it induces a critical off-policy bias. Although modern KV compression is often nearly lossless during standard inference, even minuscule approximation errors are drastically amplified by the inherent instability of RL optimization. Specifically, the sampler generates responses under a sparse context, whereas the learner updates parameters using the full, dense context. Existing statistical solutions, such as importance reweighting, struggle to correct this magnified bias, suffering from high gradient variance and severe sample inefficiency. |
| title | How to Compress KV Cache in RL Post-Training? Shadow Mask Distillation for Memory-Efficient Alignment |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2605.06850 |