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Guiding Language Model Reasoning with Planning Tokens
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Large language models (LLMs) have recently attracted considerable interest for their ability to perform complex reasoning tasks, such as chain-of-thought (CoT) reasoning. However, most of the existing approaches to enhance this ability rely heavily on data-driven methods, while neglecting the structural aspects of the model's reasoning capacity. To encourage a more structural generation of CoT steps, we propose a hierarchical generation scheme: we let the LM generate a planning token at the start of each reasoning step, intuitively serving as a high-level plan of the current step, and add their embeddings to the model parameters. Our approach requires a negligible increase in trainable parameters (0.001%) and can be applied through either full fine-tuning or a more parameter-efficient scheme. We demonstrate our method's effectiveness by applying it to three different LLMs, showing notable accuracy improvements across three math word problem datasets and one multihop QA dataset with respect to standard fine-tuning baselines.
Forward citations
Cited by 9 Pith papers
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Taking the most frequent answer obtained by re-running a reasoning model from intermediate subthought points beats using the final answer alone on AIME math problems.
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Token Assorted: Mixing Latent and Text Tokens for Improved Language Model Reasoning
Partially replacing chain-of-thought tokens with discrete latent tokens during fine-tuning improves LLM reasoning accuracy and reduces generated token count.
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Soft Thinking: Unlocking the Reasoning Potential of LLMs in Continuous Concept Space
A training-free method that feeds probability-weighted token embeddings back into LLMs during reasoning, improving accuracy and token efficiency on math and coding tasks.
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GaLore 2 pre-trains a Llama 7B model on 500B tokens using low-rank gradient projection, matching an 8-bit Adam baseline while using less optimizer memory.
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State Stream Transformer (SST) : Emergent Metacognitive Behaviours Through Latent State Persistence
A weighted-decay cache of FFN states applied to frozen Llama 3.1 8B weights is claimed to enable persistent latent reasoning and metacognitive-like behavior, with reported GSM-8K and ARC-Challenge gains.
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Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey
A comprehensive review that categorizes methods for shortening and adaptively triggering chain-of-thought reasoning in large language models.
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