SPARK: Learning from Verification in Speculative GRPO Rollouts
Group Relative Policy Optimization (GRPO) has become a standard approach for improving the reasoning abilities of large language models, but its training is costly because each step requires generating many long reasoning responses. Recent methods use speculative decoding to accelerate the rollouts, where a draft model proposes tokens that the target model verifies. However, we find that the draft model repeatedly misses the same tokens within a response and across responses in the same GRPO group, and the verification feedback on these misses is not used to update the draft during the rollout. Motivated by this analysis, we introduce SPARK, a lightweight self-adaptive mechanism designed ...
| Authors | Nhat Minh Pham, Duy Tung Doan, Quang Huy Phung et al. |
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| Primary category | cs.LG |
| Published | 5 August 2026 |
| Last updated at source | 14 October 2026 |
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