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ARXIV:2602.09000 · LLM TRAINING · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2602.09000LLM TRAININGSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
Introducing iGRPO, a self-feedback-driven training method enhancing LLMs for superior mathematical reasoning.
Opportunity summary
Pain Introducing iGRPO, a self-feedback-driven training method enhancing LLMs for superior mathematical reasoning.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
Introducing iGRPO, a self-feedback-driven training method enhancing LLMs for superior mathematical reasoning. Reinforcement Learning (RL) is a framework for aligning these models with task-specific rewards, improving overall quality and reliability.
Large Language Models (LLMs) have shown promise in solving complex mathematical problems, yet they still fall short of producing accurate and consistent solutions. Reinforcement Learning (RL) is a framework for aligning these models with…
ScienceToStartup currently rates this 6.0/10 on the public viability pass. In Stage 2, it appends this best draft to the original prompt and applies a GRPO-style update on draft-conditioned refinements, training the policy to…
LLM Training moved forward this cycle; last verified April 2026. Public score 6.0/10.
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Introducing iGRPO, a self-feedback-driven training method enhancing LLMs for superior mathematical reasoning.
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10.48550/arXiv.2602.09000Introducing iGRPO, a self-feedback-driven training method enhancing LLMs for superior mathematical reasoning.
Abstract
Large Language Models (LLMs) have shown promise in solving complex mathematical problems, yet they still fall short of producing accurate and consistent solutions. Reinforcement Learning (RL) is a framework for aligning these models with task-specific rewards, improving overall quality and reliability. Group Relative Policy Optimization (GRPO) is an efficient, value-function-free alternative to Proximal Policy Optimization (PPO) that leverages group-relative reward normalization. We introduce Iterative Group Relative Policy Optimization (iGRPO), a two-stage extension of GRPO that adds dynamic self-conditioning through model-generated drafts. In Stage 1, iGRPO samples multiple exploratory drafts and selects the highest-reward draft using the same scalar reward signal used for optimization. In Stage 2, it appends this best draft to the original prompt and applies a GRPO-style update on draft-conditioned refinements, training the policy to improve beyond its strongest prior attempt. Under matched rollout budgets, iGRPO consistently outperforms GRPO across base models (e.g., Nemotron-H-8B-Base-8K and DeepSeek-R1 Distilled), validating its effectiveness on diverse reasoning benchmarks. Moreover, applying iGRPO to OpenReasoning-Nemotron-7B trained on AceReason-Math achieves new state-of-the-art results of 85.62\% and 79.64\% on AIME24 and AIME25, respectively. Ablations further show that the refinement wrapper generalizes beyond GRPO variants, benefits from a generative judge, and alters learning dynamics by delaying entropy collapse. These results underscore the potential of iterative, self-feedback-based RL for advancing verifiable mathematical reasoning.
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PROBLEM
Introducing iGRPO, a self-feedback-driven training method enhancing LLMs for superior mathematical reasoning. Reinforcement Learning (RL) is a framework for aligning these models with task-specific rewards, improving overall quality and reliability.
METHOD
Large Language Models (LLMs) have shown promise in solving complex mathematical problems, yet they still fall short of producing accurate and consistent solutions. Reinforcement Learning (RL) is a framework for aligning these models with task-specific rewards, improving overall...
RESULT
ScienceToStartup currently rates this 6.0/10 on the public viability pass. In Stage 2, it appends this best draft to the original prompt and applies a GRPO-style update on draft-conditioned refinements, training the policy to improve beyond its strongest prior attempt.
WHY NOW
LLM Training moved forward this cycle; last verified April 2026. Public score 6.0/10.
Abstract-backed public claims while anchored extraction refreshes.
Introducing iGRPO, a self-feedback-driven training method enhancing LLMs for superior mathematical reasoning. Reinforcement Learning (RL) is a framework for aligning these models with task-specific rewards, improving overall quality and reliability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Large Language Models (LLMs) have shown promise in solving complex mathematical problems, yet they still fall short of producing accurate and consistent solutions. Reinforcement Learning (RL) is a framework for aligning these models with task-specific rewards, improving overall quality and reliability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 6.0/10 on the public viability pass. In Stage 2, it appends this best draft to the original prompt and applies a GRPO-style update on draft-conditioned refinements, training the policy to improve beyond its strongest prior attempt.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
LLM Training moved forward this cycle; last verified April 2026. Public score 6.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Introducing iGRPO, a self-feedback-driven training method enhancing LLMs for superior mathematical reasoning.
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LLM Training
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