S$^3$R: Fast and Faithful RL Rollouts via Self-Sparse Speculative Decoding
Fast rollout generation for long-context reinforcement learning with verifiable rewards (RLVR) requires reducing KV-cache traffic without changing the response groups used for policy optimization. In our profiled RLVR workloads, attention accounts for 72--76\% of autoregressive decoding kernel time. Sparse attention reduces these reads, but sampling directly from a sparse policy changes the response groups used for learning. For group-relative optimization, we show that even exact response-wise reweighting does not generally recover the expected dense-policy update because the advantage of each response depends on the rewards of other group members. We propose Self-Sparse Speculative Roll...
| Authors | Yuhang He, Hongyu Ge, Zihan Lin et al. |
|---|---|
| Primary category | cs.LG |
| Published | 9 October 2026 |
| Last updated at source | 9 October 2026 |
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