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Learning Self-Correction in Vision–Language Models via Rollout Augmentation

ICML 2026 · Vision-language and multimodal › Multimodal LLMs · approach: Reinforcement learning for visual reasoning · 0 citations (OpenAlex)

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Proposes Octopus, a rollout-augmentation RL framework that recombines paired pre- and post-<sc> responses from existing rollouts to create dense self-correction training signals, plus a two-stage response-masking scheme, yielding Octopus-8B on Qwen3-VL-8B.

Problem

Self-correction in VLMs is rarely learned by standard RLVR because effective wrong-to-correct transitions are extremely sparse, so the learning signal for controllable self-correction is weak, and jointly optimizing direct reasoning and self-correction creates conflicting signals.

Approach

Recombine o1 and o2 segments across rollouts in a group (n^2 pairings from n rollouts) to synthesize balanced, dense self-correction samples at no extra generation cost, then decouple learning via response masking: Stage I masks o1 and trains only o2 with KL on o1; Stage II selectively unmasks o1 only for samples with consistent correctness.

Key results

• Octopus-8B averages 71.7 across 7 benchmarks, versus 62.2 for Qwen3-VL-8B-Instruct and 70.7 for the best RLVR baseline (GSPO, n=16).
• Octopus reaches higher accuracy than GSPO with 16 rollouts while using 0.72x the training time per step (958.1 s total vs 1322.8 s for GSPO n=16).
• Ablation: removing Octopus augmentation drops average accuracy from 71.7 to 67.4, and random augmentation reaches 68.6.

Surprising findings

• Binary reward training fails to improve self-correction: accuracy before and after the self-correction token stays nearly identical throughout RL.
• Reward shaping for self-correction induces reward hacking, where the model deliberately gives an incorrect first answer followed by a trivial correction.

Limitations

• Self-correction is restricted to single-pass correction triggered by a special token, not multi-pass or tool-based correction.
• Reward shaping and naive unmasking lead to reward hacking and instability, requiring a carefully designed two-stage schedule.
• Random augmentation collapses training, so the method depends on structured pairing rules.

Experimental setup

Datasets: LLaVA-CoT ViRL-39k Geometry-3k

Benchmarks: MathVista MathVerse WeMath HallusionBench MMStar MMMU CharXiv AIME2024 MMLU-Pro

Metrics: accuracy pass@k training time per step inference latency

Baselines: GRPO DAPO GSPO SRPO Qwen3-VL-8B-Thinking MiMo-VL-7B-RL InternVL3.5-8B-RL GPT-4o OpenAI-o1 Claude-3.7-Sonnet

Builds on: GSPO GRPO SRPO DAPO

Closest papers by meaning

• Sherlock: Self-Correcting Reasoning in Vision-Language Models (NeurIPS 2025 · similarity 0.90)
• Training Language Models to Self-Correct via Reinforcement Learning (ICLR 2025 · similarity 0.90)
• SSL4RL: Revisiting Self-supervised Learning as Intrinsic Reward for Visual-Language Reasoning (ICML 2026 · similarity 0.89)
• NoisyRollout: Reinforcing Visual Reasoning with Data Augmentation (NeurIPS 2025 · similarity 0.89)
• Learn to Think: Improving Multimodal Reasoning through Vision-Aware Self-Improvement Training (ICML 2026 · similarity 0.88)
• Towards Self-Refinement of Vision-Language Models with Triangular Consistency (NeurIPS 2025 · similarity 0.88)