5arXiv cs.CL (Computation and Language)·
Instruction tuning plus model merging improves reasoning LMs in verifiable and unverifiable domains
A new arXiv preprint proposes a two-step method for adapting reasoning language models (RLMs) to domains lacking reliable verifiers: first apply standard supervised fine-tuning (without reasoning traces) on the target domain, then merge the resulting model with the original RLM to recover reasoning behavior. The technique is evaluated on both verifiable domains (coding) and hard-to-verify domains (text summarization), showing improvements while preserving general RLM capabilities. Notably, the authors report the full adaptation can be achieved for under $3, making it highly accessible.
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Learning to Reason with LLMs
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Steerable Model Merging (ST-Merge) improves multilingual reasoning via adaptive gated cross-attention
Researchers propose ST-Merge, a framework for adaptively merging a multilingual model and a reasoning model using a gated cross-attention mechanism that weights each source model's contribution based on input characteristics. The approach addresses the limitation of static one-size-fits-all merging strategies that fail to resolve conflicts between source models. Experiments across 21 languages on four multilingual reasoning benchmarks show consistent improvements over strong baselines.