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

2026-06-17 · Source: Takara TLDR - Daily AI Papers · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Data Science & Analytics · Depth: Expert, medium

Summary

SC-GRPO (Self-Conditioned GRPO) is a novel method addressing limitations in reinforcement learning with verifiable rewards (RLVR) for large language models (LLMs) on reasoning tasks. Traditional methods like GRPO assign uniform credit across tokens, inefficiently distributing gradients, while existing token-level credit assignment often requires external resources such as process reward models or ground-truth answers. SC-GRPO leverages the observation that conditioning a model on its own verified trajectories creates a measurable per-token KL divergence between original and conditioned distributions. This KL divergence is then applied as a multiplicative weight on GRPO gradients. Across five benchmarks spanning math, code, and agentic tasks, SC-GRPO consistently outperforms GRPO by 8.1%, DAPO by 5.9%, and also achieves higher performance than OPD, demonstrating stronger out-of-distribution capabilities.

Key takeaway

For AI scientists and machine learning engineers developing LLMs for complex reasoning, SC-GRPO offers a significant advancement in credit assignment. By leveraging self-conditioned KL divergence to weight GRPO gradients, you can achieve more precise learning without relying on expensive external supervision. Consider integrating SC-GRPO to improve your models' performance on math, code, and agentic tasks, especially when out-of-distribution robustness is critical. This approach enhances training efficiency and reasoning accuracy.

Key insights

SC-GRPO improves LLM reasoning by using self-conditioned KL divergence for fine-grained credit assignment in RLVR.

Principles

• Uniform credit assignment in RLVR wastes gradient on routine tokens.
• Conditioning a model on its own verified trajectories induces measurable per-token KL divergence.
• Distilling from a self-teacher with multiple verified trajectories leads to infeasible solutions.

Method

SC-GRPO uses per-token KL divergence, derived from self-conditioned verified trajectories, as a multiplicative weight on GRPO gradients to refine credit assignment.

In practice

• Apply SC-GRPO to enhance LLM performance on math and code reasoning.
• Improve out-of-distribution generalization for agentic tasks.

Topics

• Reinforcement Learning
• Large Language Models
• Credit Assignment
• KL Divergence
• SC-GRPO
• Reasoning Tasks

Best for: Research Scientist, 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 RLVR credit assignment by using self-conditioned KL divergence to weight gradients, outperforming uniform methods.
• 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.
• CAST: Non-Privileged Clipped Asymmetric Self-Teaching with Advantage Flipping for GRPO — CAST enhances GRPO-style RLVR by using an answer-free self-teacher and bidirectional advantage flipping for dense, aligned token-level feedback.
• 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.
• Learning from Failures: Correction-Oriented Policy Optimization with Verifiable Rewards — CIPO enhances RLVR by transforming failed trajectories into self-correction supervision, improving LLM reasoning.
• 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.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Takara TLDR - Daily AI Papers.