ReasonAny: Incorporating Reasoning Capability to Any Model via Simple and Effective Model Merging (2026.acl-long)
Copied to clipboard
| Challenge: | Existing models with long chain-of-thought reasoning lack reasoning depth and domain-specific utility. |
| Approach: | They propose a model merging framework that integrates reasoning with domain-specific task models. |
| Outcome: | The proposed model merging framework outperforms state-of-the-art models while maintaining robust reasoning performance. |
Similar Papers
Superficial Self-Improved Reasoners Benefit from Model Merging (2025.emnlp-main)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) rely heavily on large-scale reasoning data, but as data becomes scarce, model self-improvement offers a promising alternative. |
| Approach: | They propose to merge the weights of original and self-improved LLMs to mitigate model collapse and improve generalized reasoning capability. |
| Outcome: | The proposed model merge mitigates model collapse and improves generalized reasoning capability. |
Split-Merge: Scalable and Memory-Efficient Merging of Expert LLMs (2025.emnlp-main)
Copied to clipboard
| Challenge: | a zero-shot merging framework for large language models consolidates specialized domain experts into a single model without any further training. |
| Approach: | They propose a zero-shot merging framework that consolidates specialized domain experts into a single model without further training. |
| Outcome: | Experiments on code generation, mathematical reasoning, medical question answering, and instruction-following benchmarks confirm the versatility and effectiveness of the proposed framework. |
Enhancing Multilingual Reasoning via Steerable Model Merging (2026.findings-acl)
Copied to clipboard
Zhuoran Li, Rui Xu, Jian Yang, Junnan Liu, Zhijun Chen, Qianren Mao, Hongcheng Guo, Jiaheng Liu, Likang Xiao, Ming LI, Xiaojie Wang
| Challenge: | Model merging is an effective technique for composing the capabilities of a multilingual model and a reasoning model. |
| Approach: | They propose a model merging framework that modulates the contribution of each source model. |
| Outcome: | Experiments show that the proposed model merging framework outperforms strong baselines on multilingual reasoning benchmarks across 21 different languages. |
Beyond ’Aha!’: Toward Systematic Meta-Abilities Alignment in Large Reasoning Models (2026.findings-acl)
Copied to clipboard
| Challenge: | Prior work has shown that outcome-based reinforcement learning (RL) can incidentally elicit advanced reasoning behaviors such as self-correction, backtracking, and verification. |
| Approach: | They explicitly align large reasoning models with three meta-abilities: deduction, induction, and abduction, using automatically generated, self-verifiable tasks. |
| Outcome: | The proposed model aligns models with deduction, induction, and abduction meta-abilities using automatically generated, self-verifiable tasks. |
Logit Arithmetic Elicits Long Reasoning Capabilities Without Training (2026.findings-acl)
Copied to clipboard
Yunxiang Zhang, Muhammad Khalifa, Lechen Zhang, Xin Liu, Ayoung Lee, Xinliang Frederick Zhang, Farima Fatahi Bayat, Lu Wang
| Challenge: | Large reasoning models exhibit long chain-of-thought reasoning with complex strategies such as backtracking and self-verification, yet, these capabilities typically require resource-intensive post-training. |
| Approach: | They propose a decoding-time approach which transfers long chain-of-thought reasoning capabilities from a substantially smaller reasoning guider to a large non-reasoning target. |
| Outcome: | The proposed method improves performance over a model 21x smaller than the target model by 21.5% and 24.2% over the model. |
MIND: From Passive Mimicry to Active Reasoning through Capability-Aware Multi-Perspective CoT Distillation (2026.acl-long)
Copied to clipboard
Jin Cui, Jiaqi Guo, Jiepeng Zhou, Ruixuan Yang, Jiayi Lu, Jiajun Xu, Jiangcheng Song, Boran Zhao, Pengju Ren
| Challenge: | Existing approaches restrict students to following a single golden rationale and treat different reasoning paths independently, causing suboptimal performance. |
| Approach: | They propose a capability-adaptive framework that transitions distillation from passive mimicry to active cognitive construction and employ a feedback-driven inertia calibration mechanism to align supervision with the student’s current adaptability. |
| Outcome: | Experiments show that the proposed framework achieves state-of-the-art performance on both in-distribution and out-of distribution benchmarks. |
LM-Cocktail: Resilient Tuning of Language Models via Model Merging (2024.findings-acl)
Copied to clipboard
| Challenge: | Pre-trained language models are continually fine-tuned to better support downstream applications. however, this operation may result in significant performance degeneration on general perspectives. |
| Approach: | They propose a method which enables pre-trained language models to stay resilient in general perspectives. |
| Outcome: | The proposed model achieves strong empirical performance in the whole scope of general tasks while preserving a superior capacity in its targeted domain. |
ThinkPilot: Steering Reasoning Models via Automated Think-prefixes Optimization (2026.findings-eacl)
Copied to clipboard
| Challenge: | Large Reasoning Models (LRMs) are powerful but still suffer from inefficient and off-target reasoning. |
| Approach: | They propose a training-free framework that automatically optimizes Large Reasoning Models' reasoning by generating think-prefixes that evolve driven by a taxonomy of reasoning behaviors. |
| Outcome: | The proposed framework significantly improves accuracy-length trade-off for efficient reasoning, drastically improves safety and improves instruction following. |
Mergenetic: a Simple Evolutionary Model Merging Library (2025.acl-demo)
Copied to clipboard
| Challenge: | Recent work shows that combining model merging with evolutionary algorithms can boost performance, but there is currently no library for experimenting with different evolutionary algorithms and merging methods. |
| Approach: | They propose an open-source library for evolutionary model merging that enables easy composition of merging methods and evolutionary algorithms while incorporating lightweight fitness estimators to reduce evaluation costs. |
| Outcome: | The proposed library produces competitive results across languages and tasks using modest hardware. |
Exploring Self-supervised Logic-enhanced Training for Large Language Models (2024.naacl-long)
Copied to clipboard
| Challenge: | Traditional attempts to enhance the logical reasoning abilities of language models often rely on supervised fine-tuning, limiting their generalization to new tasks or domains. |
| Approach: | They propose a framework for integrating logical reasoning capabilities into LLMs and activating them via in-context learning. |
| Outcome: | The proposed framework achieves comparable results to existing models on three language understanding benchmarks. |