From Failure to Feedback: Group Revision Unlocks Hard Cases in Object-Level Grounding

• URL: http://arxiv.org/abs/2605.15951v1
• Date: Fri, 15 May 2026 13:41:41 GMT
• Title: From Failure to Feedback: Group Revision Unlocks Hard Cases in Object-Level Grounding
• Abstract summary: We propose a group-revision optimisation paradigm that enhances learning on hard cases.<n>Inspired by reward shaping, we introduce a consolidation process that quantifies each candidate's improvement over the initial attempt.<n>Our method achieves consistent gains across referring and reasoning segmentation, REC, and counting benchmarks compared with prior GRPO-based models.
• Score: 35.008790179255136
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Finetuning Large Vision-Language Models with reinforcement learning has emerged as a promising approach to enhance their capability in object-level grounding. However, existing methods, mainly based on GRPO, assign rewards at the response level. Such sparse reward, often criterion-induced, leads to minimal learning signals when all candidate responses fail in challenging scenarios. In this work, we propose a group-revision optimisation paradigm that enhances learning on hard cases. It begins with a sampled initial response and generates a set of revised candidates to explore improved grounding outcomes. Inspired by reward shaping, we introduce a consolidation process that quantifies each candidate's improvement over the initial attempt and converts it into informative shaping signals. These signals are used to both refine the reward and modulate the advantage, amplifying the influence of high-quality revisions. Our method achieves consistent gains across referring and reasoning segmentation, REC, and counting benchmarks compared with prior GRPO-based models. Our code is available at https://github.com/yyliu01/GroupRevision.

Related papers

• ConsensusBench: Benchmark of Consensus Nodes for LLM Reasoning via Outcome Reward Densifying [45.2850302643347]
  We develop a new reinforcement learning signal named ConsensusPR.<n>It directly reduces the reward sparsity of outcome reward across long reasoning trajectories.<n> Experiments across AIME 2024, AIME 2025, GSM8K, MATH-500, and our ConsensusBench demonstrate that the proposed method consistently surpasses GRPO-style approaches.
  arXiv  Detail & Related papers  (2026-09-04T02:29:10Z)
• Too Correct to Learn: Reinforcement Learning on Saturated Reasoning Data [55.84428098924793]
  We propose Constrained Uniform Top-K Sampling (CUTS), a parameter-free decoding strategy enforcing structure-preserving exploration.<n>We integrate this into Mixed-CUTS, a training framework synergizing exploitative and exploratory rollouts to amplify intra-group advantage variance.<n> Notably, Mixed-CUTS improves Pass@1 accuracy on the challenging AIME25 benchmark by up to 15.1% over standard GRPO.
  arXiv  Detail & Related papers  (2026-04-20T16:43:28Z)
• 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)
• From Absolute to Relative: Rethinking Reward Shaping in Group-Based Reinforcement Learning [7.6602542594279335]
  We propose Reinforcement Learning with Relative Rewards to shift reward shaping from absolute scoring to relative ranking.<n>We show that RLRR yields consistent performance improvements over standard group-based baselines across reasoning benchmarks and open-ended generation tasks.
  arXiv  Detail & Related papers  (2026-01-30T15:07:06Z)
• GCPO: When Contrast Fails, Go Gold [6.596504114809683]
  We introduce Group Contrastive Policy Optimization (GCPO), a method that incorporates external standard reference answers.<n>When the model cannot solve a problem, the reference answer supplies the correct response, steering the model toward an unequivocally accurate update direction.<n>GCPO achieves outstanding results across multiple benchmark datasets, yielding substantial improvements over the baseline model.
  arXiv  Detail & Related papers  (2025-10-09T05:09:06Z)
• EDGE-GRPO: Entropy-Driven GRPO with Guided Error Correction for Advantage Diversity [7.818698554631196]
  Group Relative Policy Optimization (GRPO) algorithm relies on sparse reward rules, leading to the advantage collapse problem.<n>We propose the EDGE-GRPO algorithm, which adopts textbfEntropy-textbfDriven Advantage and textbfGuided textbfError Correction to effectively mitigate the problem of advantage collapse.
  arXiv  Detail & Related papers  (2025-07-29T14:23:58Z)
• Intra-Trajectory Consistency for Reward Modeling [67.84522106537274]
  We develop an intra-trajectory consistency regularization to enforce that adjacent processes with higher next-token generation probability maintain more consistent rewards.<n>We show that the reward model trained with the proposed regularization induces better DPO-aligned policies and achieves better best-of-N (BON) inference-time verification results.
  arXiv  Detail & Related papers  (2025-06-10T12:59:14Z)
• 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)
• Chain-of-Retrieval Augmented Generation [91.02950964802454]
  This paper introduces an approach for training o1-like RAG models that retrieve and reason over relevant information step by step before generating the final answer.<n>Our proposed method, CoRAG, allows the model to dynamically reformulate the query based on the evolving state.
  arXiv  Detail & Related papers  (2025-01-24T09:12:52Z)
• Take the Bull by the Horns: Hard Sample-Reweighted Continual Training Improves LLM Generalization [165.98557106089777]
  A key challenge is to enhance the capabilities of large language models (LLMs) amid a looming shortage of high-quality training data. Our study starts from an empirical strategy for the light continual training of LLMs using their original pre-training data sets. We then formalize this strategy into a principled framework of Instance-Reweighted Distributionally Robust Optimization.
  arXiv  Detail & Related papers  (2024-02-22T04:10:57Z)

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.