Provable Benefits of RLVR over SFT for Reasoning Models: Learning to Backtrack Efficiently

2026-06-22 · Source: Takara TLDR - Daily AI Papers · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, medium

Summary

Stanley Wei and Juno Kim's research, "Provable Benefits of RLVR over SFT for Reasoning Models: Learning to Backtrack Efficiently," theoretically demonstrates why reinforcement fine-tuning with verifiable rewards (RLVR) significantly enhances large language models' (LLMs) reasoning performance compared to supervised fine-tuning (SFT). The authors model chain-of-thought (CoT) reasoning as a graph pathfinding problem. They prove that SFT, when trained exclusively on optimal "golden shortest paths" without negative examples, fails to develop efficient backtracking capabilities. In contrast, RLVR-trained models learn to efficiently backtrack from dead ends using only outcome rewards. This fundamental difference results in an exponential separation in inference-time compute efficiency between RLVR and SFT. RLVR enables models to identify critical decision points in a reasoning chain, leading to better allocation of inference-time computational resources. Furthermore, the reasoning traces generated by an RLVR model can be distilled to impart efficient backtracking skills to a base model.

Key takeaway

For Machine Learning Engineers optimizing LLM reasoning performance, this research indicates that relying solely on supervised fine-tuning (SFT) for chain-of-thought tasks will result in inefficient backtracking and higher inference costs. You should prioritize reinforcement learning with verifiable rewards (RLVR) to train models that efficiently navigate complex reasoning paths and manage computational resources. Consider distilling RLVR-trained model traces to impart these backtracking efficiencies to your base models, ensuring better inference-time compute allocation.

Key insights

RLVR's outcome-based rewards enable LLMs to learn efficient backtracking, unlike SFT, leading to exponential inference compute gains.

Principles

• SFT on optimal paths neglects backtracking.
• RLVR learns backtracking from dead ends.
• Outcome rewards guide efficient decision learning.

Method

The paper models CoT reasoning as graph pathfinding to compare RLVR and SFT. It proves SFT's backtracking failure and RLVR's efficiency, then distills RLVR traces.

In practice

• Distill RLVR traces for base model training.
• Prioritize RLVR for compute-efficient reasoning.
• Design SFT datasets with negative examples.

Topics

• Reinforcement Learning with Verifiable Rewards
• Supervised Fine-Tuning
• LLM Reasoning
• Chain-of-Thought
• Backtracking Algorithms
• Inference Optimization

Code references

• zhichul/rlcausal
• Qwen-Applications/CLIPO

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

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