| Challenge: | Existing compact reasoning approaches generate long reasoning chains, but they lack a mechanism to encode token-level functional importance for answer generation. |
| Approach: | They propose a procedure that iteratively removes reasoning tokens from models and prunes them to yield length-controlled reasoning chains. |
| Outcome: | The proposed procedure outperforms a frontier model at reasoning lengths and shows that attention scores predict greedy pruning ranks. |
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Daewon Choi, Jimin Lee, Jihoon Tack, Woomin Song, Saket Dingliwal, Sai Muralidhar Jayanthi, Bhavana Ganesh, Jinwoo Shin, Aram Galstyan, Sravan Babu Bodapati
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Your Reasoning Model Knows What Counts: Self-Guided Chain-of-Thought Pruning for Efficient Reasoning (2026.acl-long)
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| Challenge: | Existing approaches to Chain-of-Thought reasoning are often degraded because they disregard the model’s intrinsic reasoning dependency. |
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Not All Tokens Matter: Towards Efficient LLM Reasoning via Token Significance in Reinforcement Learning (2026.acl-long)
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| Challenge: | Large language models (LLMs) often produce unnecessarily long explanations that reduce efficiency. |
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| Challenge: | Existing heuristics fail to capture global causal logic due to rigid rules and limited search spaces. |
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Beyond Token Length: Step Pruner for Efficient and Accurate Reasoning in Large Language Models (2026.findings-acl)
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| Challenge: | Existing reinforcement learning methods for large reasoning models suffer from excessive verbosity, known as "overthinking." Existing models penalize generated tokens to promote conciseness, but these methods encounter two challenges: they may develop hacking behavior in later stages of training by discarding reasoning steps. |
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Forget What Matters, Keep the Rest: Selective Unlearning of Informative Tokens (2026.acl-long)
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| Challenge: | Recent studies have explored token-wise loss regularizers that prioritize informative tokens, but rely on ground-truth confidence or external linguistic parsers, which limits their ability to capture contextual information or the model’s overall predictive state. |
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Verbosity-Aware Rationale Reduction: Sentence-Level Rationale Reduction for Efficient and Effective Reasoning (2025.findings-acl)
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| Challenge: | Recent advances in Large Language Models (LLMs) demonstrate remarkable reasoning capabilities comparable to human cognitive abilities. |
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| Challenge: | Efficient long-context inference remains a major challenge for large language models (LLMs), as the cost of attention computation during auto-regressive decoding grows linearly with the context length. |
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Attention Score is not All You Need for Token Importance Indicator in KV Cache Reduction: Value Also Matters (2024.emnlp-main)
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| Challenge: | Recent studies have focused on scaling the context size of large language models (LLMs) however, the enormous inference costs of LLMs limit their applications. |
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Graph-Based Chain-of-Thought Pruning for Reducing Redundant Reflections in Reasoning LLMs (2026.findings-acl)
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Hongyuan Yuan, Xinran He, Run Shao, Bolei He, Xianwei Xue, Mengke Chen, Qiutong Pan, Haiwei Wang, Haifeng Li
| Challenge: | Extending CoT through RL can induce undesirable thinking patterns such as overthinking . prior work has focused on inefficient reflection, which manifests in two problematic patterns: Indiscriminate Reflection and Repetitive Reflectione . |
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