G$^2$RPO-A: Guided Group Relative Policy Optimization with Adaptive Guidance

• URL: http://arxiv.org/abs/2508.13023v1
• Date: Mon, 18 Aug 2025 15:41:16 GMT
• Title: G$^2$RPO-A: Guided Group Relative Policy Optimization with Adaptive Guidance
• Authors: Yongxin Guo, Wenbo Deng, Zhenglin Cheng, Xiaoying Tang,
• Abstract summary: We investigate Guided GRPO, which injects ground-truth reasoning steps into roll-out trajectories.<n>We find that naively adding guidance delivers limited gains.<n>Experiments on mathematical reasoning and code-generation benchmarks confirm that G$2$RPO-A substantially outperforms vanilla GRPO.
• Score: 1.0591274452539035
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has markedly enhanced the reasoning abilities of large language models (LLMs). Its success, however, largely depends on strong base models with rich world knowledge, yielding only modest improvements for small-size language models (SLMs). To address this limitation, we investigate Guided GRPO, which injects ground-truth reasoning steps into roll-out trajectories to compensate for SLMs' inherent weaknesses. Through a comprehensive study of various guidance configurations, we find that naively adding guidance delivers limited gains. These insights motivate G$^2$RPO-A, an adaptive algorithm that automatically adjusts guidance strength in response to the model's evolving training dynamics. Experiments on mathematical reasoning and code-generation benchmarks confirm that G$^2$RPO-A substantially outperforms vanilla GRPO. Our code and models are available at https://github.com/T-Lab-CUHKSZ/G2RPO-A.

Related papers

• iGRPO: Self-Feedback-Driven LLM Reasoning [88.83313431248473]
  Large Language Models (LLMs) have shown promise in solving complex mathematical problems, yet they still fall short of producing accurate and consistent solutions.<n>We introduce Iterative Group Relative Policy Optimization (iGRPO), a two-stage extension of GRPO that adds dynamic self-conditioning through model-generated drafts.<n>Under matched rollout budgets, iGRPO consistently outperforms GRPO across base models.
  arXiv  Detail & Related papers  (2026-02-09T18:45:11Z)
• Generative Actor Critic [74.04971271003869]
  Generative Actor Critic (GAC) is a novel framework that decouples sequential decision-making by reframing textitpolicy evaluation as learning a generative model of the joint distribution over trajectories and returns.<n>Experiments on Gym-MuJoCo and Maze2D benchmarks demonstrate GAC's strong offline performance and significantly enhanced offline-to-online improvement compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-12-25T06:31:11Z)
• Token-Regulated Group Relative Policy Optimization for Stable Reinforcement Learning in Large Language Models [18.785063555637613]
  Group Relative Policy Optimization (GRPO) has demonstrated strong performance.<n>It suffers from a critical issue: low-probability tokens disproportionately dominate gradient updates.<n>This imbalance leads to unstable training and suppresses the contribution of high-probability tokens.
  arXiv  Detail & Related papers  (2025-10-29T08:07:47Z)
• $λ$-GRPO: Unifying the GRPO Frameworks with Learnable Token Preferences [22.199479724764725]
  We introduce a learnable parameter $lambda$ that adaptively controls token-level weighting.<n>We find that $lambda$-GRPO achieves consistent improvements over vanilla GRPO and DAPO.<n>These gains come without any modifications to the training data or additional computational cost.
  arXiv  Detail & Related papers  (2025-10-08T10:39:07Z)
• Token Hidden Reward: Steering Exploration-Exploitation in Group Relative Deep Reinforcement Learning [64.04741347596938]
  We introduce Token Hidden Reward (THR), a token-level metric that quantifies each token's influence on the likelihood of correct responses.<n>We find that training dynamics are dominated by a small subset of tokens with high absolute THR values.<n>This insight suggests a THR-guided reweighting algorithm that modulates GRPO's learning signals to explicitly bias training toward exploitation or exploration.
  arXiv  Detail & Related papers  (2025-10-04T04:49:44Z)
• GRPO-$λ$: Credit Assignment improves LLM Reasoning [35.452488047246646]
  We present GRPO-$lambda$, a novel extension to GRPO that enhances credit assignment in RL finetuning of LLMs for complex reasoning tasks.<n>We compare GRPO-$lambda$ against GRPO by training models from 1.5B to 7B parameters on $4$ different math reasoning datasets.<n>With GRPO-$lambda$, the resulting average performance on AIME24, Math500, OlympiadMath, MinervaMath, and AMC improves over GRPO by over $3$ points and a $4.5$ points improvement on the 7B model.
  arXiv  Detail & Related papers  (2025-09-30T19:11:10Z)
• GRPO-CARE: Consistency-Aware Reinforcement Learning for Multimodal Reasoning [53.894789613838654]
  We introduce SEED-Bench-R1, a benchmark with complex real-world videos requiring balanced perception and reasoning.<n>Using SEED-Bench-R1, we find that standard GRPO, while improving answer accuracy, often reduces logical coherence between reasoning steps and answers, with only a 57.9% consistency rate.<n>We propose GRPO-CARE, a consistency-aware RL framework optimizing both answer correctness and reasoning coherence without explicit supervision.
  arXiv  Detail & Related papers  (2025-06-19T08:49:13Z)
• Adaptive Guidance Accelerates Reinforcement Learning of Reasoning Models [3.207886496235499]
  We study the process through which reasoning models trained with reinforcement learning on verifiable rewards (RLVR) can learn to solve new problems.<n>We find that RLVR drives performance in two main ways: (1) by compressing pass@$k$ into pass@1 and (2) via "capability gain" in which models learn to solve new problems that they previously could not solve even at high $k$.
  arXiv  Detail & Related papers  (2025-06-16T19:03:06Z)
• VerIPO: Cultivating Long Reasoning in Video-LLMs via Verifier-Gudied Iterative Policy Optimization [59.39976343879587]
  VerIPO aims to gradually improve video LLMs' capacity for generating deep, long-term reasoning chains.<n>The training loop benefits from GRPO's expansive search and DPO's targeted optimization.<n>Our trained models exceed the direct inference of large-scale instruction-tuned Video-LLMs.
  arXiv  Detail & Related papers  (2025-05-25T06:41:28Z)
• DisCO: Reinforcing Large Reasoning Models with Discriminative Constrained Optimization [55.06360285372418]
  Group Relative Policy Optimization is a reinforcement learning method for large reasoning models (LRMs)<n>In this work, we analyze the GRPO objective under a binary reward setting and reveal an inherent limitation of question-level difficulty bias.<n>We introduce a new Discriminative Constrained Optimization framework for reinforcing LRMs, grounded in the principle of discriminative learning.
  arXiv  Detail & Related papers  (2025-05-18T11:08:32Z)
• Stepwise Guided Policy Optimization: Coloring your Incorrect Reasoning in GRPO [22.00487909203855]
  Group Relative Policy Optimization fails to update a policy when all responses within a group are incorrect.<n>This limitation underscores a key gap between artificial and human intelligence.<n>We introduce a simple framework that mitigates the all-negative-sample issue by incorporating response diversity within groups.
  arXiv  Detail & Related papers  (2025-05-16T18:02:05Z)
• Not All Rollouts are Useful: Down-Sampling Rollouts in LLM Reinforcement Learning [55.15106182268834]
  Reinforcement learning with verifiable rewards (RLVR) has emerged as the leading approach for enhancing reasoning capabilities in large language models.<n>It faces a fundamental compute and memory asymmetry: rollout generation is embarrassingly parallel and memory-light, whereas policy updates are communication-heavy and memory-intensive.<n>We introduce PODS (Policy Optimization with Down-Sampling), which decouples rollout generation from policy updates by training only on a strategically selected subset of rollouts.
  arXiv  Detail & Related papers  (2025-04-18T17:49:55Z)
• A Minimalist Approach to LLM Reasoning: from Rejection Sampling to Reinforce [68.99924691391048]
  We revisit GRPO from a reinforce-like algorithm perspective and analyze its core components.<n>We find that a simple rejection sampling baseline, RAFT, yields competitive performance than GRPO and PPO.<n>Motivated by this insight, we propose Reinforce-Rej, a minimal extension of policy gradient that filters both entirely incorrect and entirely correct samples.
  arXiv  Detail & Related papers  (2025-04-15T16:15:02Z)
• GPG: A Simple and Strong Reinforcement Learning Baseline for Model Reasoning [17.544255491384046]
  We propose a minimalist RL approach termed Group Policy Gradient (GPG)<n>Unlike conventional methods, GPG directly optimize the original RL objective, thus obviating the need for surrogate loss functions.<n>Our approach achieves superior performance without relying on auxiliary techniques or adjustments.
  arXiv  Detail & Related papers  (2025-04-03T12:53:41Z)
• Monte Carlo Tree Search Boosts Reasoning via Iterative Preference Learning [55.96599486604344]
  We introduce an approach aimed at enhancing the reasoning capabilities of Large Language Models (LLMs) through an iterative preference learning process. We use Monte Carlo Tree Search (MCTS) to iteratively collect preference data, utilizing its look-ahead ability to break down instance-level rewards into more granular step-level signals. The proposed algorithm employs Direct Preference Optimization (DPO) to update the LLM policy using this newly generated step-level preference data.
  arXiv  Detail & Related papers  (2024-05-01T11:10:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.