Learning from Own Solutions: Self-Conditioned Credit Assignment for Reinforcement Learning with Verifiable Rewards

2026-06-17 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

SC-GRPO ("Self-Conditioned GRPO") is a new method for reinforcement learning with verifiable rewards (RLVR) that addresses the uniform credit assignment issue prevalent in existing methods like GRPO. While GRPO assigns uniform credit, wasting gradient on routine tokens and under-crediting pivotal reasoning steps, SC-GRPO uses KL divergence as a multiplicative weight on GRPO gradients. This divergence is induced by conditioning the model on its own verified trajectories, a technique that avoids external teachers or privileged information required by other token-level credit assignment methods like On-Policy Distillation. SC-GRPO consistently outperforms GRPO by 8.1% and DAPO by 5.9% across five benchmarks spanning math, code, and agentic tasks, also achieving higher performance than OPD with stronger out-of-distribution (OOD) performance.

Key takeaway

For Machine Learning Engineers developing LLMs for reasoning tasks with verifiable rewards, you should consider integrating SC-GRPO to overcome the limitations of uniform credit assignment. This method offers a significant performance boost, outperforming GRPO by 8.1% and DAPO by 5.9%, while also providing stronger out-of-distribution capabilities without requiring external teachers or privileged information. Evaluate its impact on your specific math, code, or agentic task benchmarks.

Key insights

SC-GRPO improves RLVR credit assignment by using self-conditioned KL divergence to weight gradients, outperforming uniform methods.

Principles

• Uniform credit assignment wastes gradient on routine tokens.
• Self-conditioning on verified trajectories induces measurable per-token KL divergence.
• Distilling from a self-teacher with multiple verified trajectories leads to infeasible weighted-average solutions.

Method

SC-GRPO uses KL divergence, derived from conditioning the model on its own verified trajectories, as a multiplicative weight on GRPO gradients to improve token-level credit assignment.

In practice

• Apply to math reasoning tasks.
• Enhance code generation models.
• Improve agentic task performance.

Topics

• Reinforcement Learning
• LLM Training
• Credit Assignment
• SC-GRPO
• Verifiable Rewards
• KL Divergence

Best for: Research Scientist, AI Engineer, AI Scientist, Machine Learning Engineer, NLP Engineer

Related on AIssential

• Learning from Own Solutions: Self-Conditioned Credit Assignment for Reinforcement Learning with Verifiable Rewards — SC-GRPO improves LLM reasoning by using self-conditioned KL divergence for fine-grained credit assignment in RLVR.
• Overcoming reward signal challenges: Verifiable rewards-based reinforcement learning with GRPO on SageMaker AI — RLVR with GRPO and few-shot examples significantly improves LLM accuracy on verifiable tasks.
• Memory-R2: Fair Credit Assignment for Long-Horizon Memory-Augmented LLM Agents — Fair credit assignment and progressive training are crucial for long-horizon memory-augmented LLM agents.
• Enhancing LLM-based Search Agents via Contribution Weighted Group Relative Policy Optimization — CW-GRPO improves LLM search agents by using an LLM judge for fine-grained, per-round credit assignment.
• Reducing Credit Assignment Variance via Counterfactual Reasoning Paths — Counterfactual trajectory comparison enables process-level credit assignment, reducing gradient variance in LLM reinforcement learning.
• DACA-GRPO: Denoising-Aware Credit Assignment for Reinforcement Learning in Diffusion Language Models — DACA-GRPO improves diffusion LLM reinforcement learning by assigning temporal credit and reducing likelihood estimation bias.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.