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Found 2 papers, 2 papers with code
Offline Reinforcement Learning for LLM Multi-Step Reasoning
While Direct Preference Optimization (DPO) has shown promise in aligning LLMs with human preferences, it is less suitable for multi-step reasoning tasks because (1) DPO relies on paired preference data, which is not readily available for multi-step reasoning tasks, and (2) it treats all tokens uniformly, making it ineffective for credit assignment in multi-step reasoning tasks, which often come with sparse reward.
BitNet: Scaling 1-bit Transformers for Large Language Models
The increasing size of large language models has posed challenges for deployment and raised concerns about environmental impact due to high energy consumption.
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