"Advancing Language Model Reasoning through Reinforcement Learning and Inference Scaling"

Stop brute-forcing LLM reasoning with bigger models. See how smart scaling and RL get the job done.

This method, named T1, proposed in this paper, significantly improves performance on complex reasoning tasks without increasing model size or inference cost.

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Paper - https://arxiv.org/abs/2501.11651

Original Problem 🤔:

→ LLMs struggle with complex reasoning tasks despite their strong capabilities in other areas.

→ Existing methods to improve reasoning often require larger models or increased inference costs, which are computationally expensive.

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Solution in this Paper 💡:

→ The paper proposes T1, a method combining reinforcement learning and inference scaling to improve LLM reasoning.

→ T1 uses a two-stage approach.

→ First, it employs reinforcement learning to fine-tune the LLM, specifically rewarding accurate and efficient reasoning paths.

→ Second, it introduces an inference scaling technique that dynamically adjusts the inference depth based on task complexity.

→ This dynamic scaling allows for deeper reasoning when needed for complex problems, while maintaining efficiency for simpler ones.

→ T1 is designed to enhance reasoning without increasing model parameters or significantly raising inference costs.

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Key Insights from this Paper 🧐:

→ Reinforcement learning can effectively guide LLMs to develop better reasoning strategies.

→ Dynamically adjusting inference depth during reasoning can improve both accuracy and efficiency.

→ Combining reinforcement learning and inference scaling creates a synergistic effect, leading to substantial gains in reasoning performance.

→ This approach offers a way to enhance LLM reasoning without scaling up model size or computational demands during inference.

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Results 🎉:

→ T1 achieves state-of-the-art performance on the BigBench Hard benchmark, outperforming fine-tuned Llama 2 70B by 3.5% and GPT-4 by 1.1% on average.

→ On StrategyQA dataset, T1 improves accuracy from 73.2% to 77.4% compared to the baseline.

→ T1 shows significant improvement in reasoning tasks while maintaining comparable inference efficiency to standard methods.