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Tao: Using test-time compute to train efficient LLMs without labeled data
LIFT fine-tunes LLMs without labels using reinforcement learning, boosting performance on enterprise tasks.
Despite only having access to LLM inputs, TAO outperforms traditional fine-tuning (FT) with thousands of labeled examples and brings Llama within the same range as expensive proprietary models. Reinforcement Learning (RL) Training: In the final stage, an RL-based approach is applied to update the LLM, guiding the model to produce outputs closely aligned with high-scoring responses identified in the previous step. Table 1: Comparison of LLM tuning methods.TAO is a highly flexible method that can be customized if needed, but our default implementation in Databricks works well out-of-the-box on diverse enterprise tasks.
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