arXiv subjects

Quang Huy Phung

Publications and source records attributed to Quang Huy Phung.

2 recordsLinked to original sources

arXiv2026

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 to enhance draft models used in speculative GRPO training. SPARK leverages target verification feedback to refine draft predictions during generation while preserving their existing training procedures. Averaged across 21 model-dataset settings, SPARK improves generation speedup by 32.7% and 11.4%, and end-to-end GRPO training speedup by 32.2% and 8.5%, over EAGLE3-style and DFlash-style baselines, respectively.

cs.LG↗

arXiv2026

CollabCoder: Plan-Code Co-Evolution via Collaborative Decision-Making for Efficient Code Generation

Automated code generation remains a persistent challenge in software engineering, as conventional multi-agent frameworks are often constrained by static planning, isolated execution, high computational overhead, and limited adaptability to complex tasks. This paper introduces CollabCoder, a novel Plan-Code Co-Evolution framework that improves code generation through dynamic multi-agent collaboration. The core idea is to design a collaborative decision-making process between the plan module and the code module to decide which module should be executed for the debugging process. Extensive experiments on widely used benchmarks demonstrate that CollabCoder consistently improves code quality and robustness across tasks. Importantly, CollabCoder achieves performance comparable to or exceeding current state-of-the-art methods while reducing computational overhead, with efficiency gains becoming more pronounced as benchmark difficulty increases. On the more challenging LiveCodeBench and xCodeEval benchmarks, our approach improves performance by 11-20% over strong baselines while reducing the number of API calls by an average of 4-10 per execution.

cs.SE↗