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Iterative Reasoning Preference Optimization
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Iterative preference optimization methods have recently been shown to perform well for general instruction tuning tasks, but typically make little improvement on reasoning tasks (Yuan et al., 2024, Chen et al., 2024). In this work we develop an iterative approach that optimizes the preference between competing generated Chain-of-Thought (CoT) candidates by optimizing for winning vs. losing reasoning steps that lead to the correct answer. We train using a modified DPO loss (Rafailov et al., 2023) with an additional negative log-likelihood term, which we find to be crucial. We show reasoning improves across repeated iterations of this scheme. While only relying on examples in the training set, our approach results in increasing accuracy on GSM8K, MATH, and ARC-Challenge for Llama-2-70B-Chat, outperforming other Llama-2-based models not relying on additionally sourced datasets. For example, we see a large improvement from 55.6% to 81.6% on GSM8K and an accuracy of 88.7% with majority voting out of 32 samples.
Forward citations
Cited by 7 Pith papers
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Test-Time Scaling via Error Localization
TTEL uses feedback-induced token probability drops to localize the first error in a failed reasoning trace and branch a new generation from that prefix, improving pass@k per token on coding and math benchmarks.
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Multi-Turn On-Policy Distillation with Prefix Replay
ReOPD offline-distills multi-turn agentic LLMs via teacher-prefix replay plus step-decay sampling, matching online OPD accuracy at ≥4× speed with zero tool calls.
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PGPO: Enhancing Agent Reasoning via Pseudocode-style Planning Guided Preference Optimization
Pseudocode-structured plans paired with preference optimization improve LLM agent success rates and generalization across interactive benchmarks.
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From Answers to Rationales: Self-Aligning Multimodal Reasoning with Answer-Oriented Chain-of-Thought
Answer-oriented chain-of-thought prompts that generate both positive and negative reasoning data, combined with iterative DPO, improve multimodal LLM reasoning on several benchmarks.
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Rethinking DPO: The Role of Rejected Responses in Preference Misalignment
BDPO replaces the rejected response probability in the DPO loss denominator with a mixture of the learned and reference policies, yielding better chosen-response probability and better benchmark scores.
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Technical Report of TeleChat2, TeleChat2.5 and T1
The released T1-115B open-weight model outperforms OpenAI's o1-mini and GPT-4o on MATH500, AlignBench, and IFEval, despite using a standard dense transformer architecture.
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Optimising Language Models for Downstream Tasks: A Post-Training Perspective
A dissertation that repackages the author's previously published papers on continued pre-training, prompt tuning, and instruction modelling into a single narrative.
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