Can AI debug its own reasoning? In this video, we explore an exciting research paper from Alibaba titled START: Self-Taught Reasoner with Tools. This approach teaches large language models (LLMs) to leverage Python during their reasoning process, enabling them to validate, debug, and refine their solutions, while thinking.

We break down the START model's two-phase training process, including Hint-Infer and Hint Rejection Sampling Fine-Tuning (Hint-RFT), a Rejection Sampling Fine-Tuning approach for LLMs to teach themselves how to leverage external tools.

Paper - https://arxiv.org/abs/2503.04625
Written Review - https://aipapersacademy.com/self-taught-reasoner-with-tools/
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The video was edited using VideoScribe - https://tidd.ly/44TZEiX
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Chapters:
0:00 Introduction
1:16 Inference: Hint-infer
4:27 Training Phase 1: Hint-RFT
5:55 Training Phase 2: RFT
6:50 Results

AI Papers Academy

Alibaba's START (Self-Taught Reasoner with Tools) paper demonstrates how LLMs can integrate Python execution into their chain-of-thought reasoning. The approach injects strategic 'hints' during inference to prompt the model to write and run Python code, then refine answers based on execution results. Training involves two phases: first, a seed dataset of 12,000 samples is curated via hint-based rejection sampling (keeping only cases where tool-augmented inference succeeds but standard inference fails), then a larger 50,000-sample dataset is used for a second fine-tuning phase. The resulting START model, based on QwQ-32B, consistently outperforms same-size baselines and even beats OpenAI's o1-preview and o1-mini on math and coding benchmarks.

START by Alibaba: Teaching LLMs to Debug Their Thinking with Python