arXiv Machine Learning
SPARK: Learning from Verification in Speculative GRPO Rollouts
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arXiv Machine Learning
Aug 6
SpecRoll: Fast-Slow Verifier-Feedback Adaptation for Speculative Reinforcement Learning Rollouts
arXiv:2608. 04962v1 Announce Type: new Abstract: Reinforcement learning (RL) post-training improves the reasoning capabilities of large language models, but autoregressive rollout generation remains a major efficiency bottleneck.
arXiv Machine Learning
Jun 18
EfficientRollout: System-Aware Self-Speculative Decoding for RL Rollouts
arXiv:2606. 18967v1 Announce Type: new Abstract: Reinforcement learning (RL) has become a representative post-training paradigm for LLMs, enabling strong reasoning and agentic capabilities.
arXiv AI
Aug 7
When Drafts Evolve: Speculative Decoding Meets Online Learning
arXiv:2603. 12617v2 Announce Type: replace-cross Abstract: Speculative decoding has emerged as a widely adopted paradigm for accelerating large language model inference, where a lightweight draft model rapidly generates candidate tokens that are then verified in parallel by a larger target model.
arXiv Computation and Language
Sep 21
Draft-OPD: On-Policy Distillation for Speculative Draft Models
Draft-OPD introduces an on‑policy distillation method for speculative draft models, addressing the mismatch between supervised fine‑tuning and inference by letting the target model supervise the drafter on draft‑induced states. The approach uses target‑assisted rollouts for stable continuations and replays drafting from error positions exposed during verification, enabling the drafter to learn from both accepted and rejected proposals. Experiments demonstrate that Draft‑OPD achieves more than five‑fold lossless acceleration across diverse tasks, outperforming prior draft models such as EAGLE‑3 and DFlash by 23 % and 13 % respectively.
arXiv AI
Oct 26
NgramRL: Accelerating RL Post-Training for Structured Generation via Rollout-Aware N-gram Speculative Decoding
NgramRL is a new method for accelerating reinforcement‑learning post‑training of large language models by using a per‑prompt n‑gram drafter built from recent rollout history, eliminating the need for auxiliary models or persistent GPU state. It incorporates an adaptive fan‑out prefetcher that predicts verification outcomes to hide CPU cache lookup and scheduling delays, and a causal budget allocator that focuses prefetches on high‑probability outcomes. Experiments on two model families and three reasoning/structured‑generation workloads show that NgramRL achieves 3.60–7.77 token savings per target‑model forward in the warm‑cache regime, up to 1.99× faster than SuffixDecoding and 7.0× faster than prompt‑lookup decoding, with the prefetcher improving throughput by 1.38× on Spider.
arXiv Computation and Language
Oct 7
DLoop: Looped Speculative Decoding
The paper introduces DLoop, a looped speculative decoding technique that adaptively performs multiple drafting stages before verification, allowing a draft model to continue generating tokens while confident. By verifying all accumulated draft tokens together and training the draft model to handle its own hidden states for unverified tokens, DLoop reduces the number of target‑model forward passes needed. Experiments across several speculative decoding methods show wall‑clock speedups of 5–41 % without sacrificing lossless decoding.