Papers with greedy pruning ranks
Do LLMs Encode Functional Importance of Reasoning Tokens ? (2026.acl-long)
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| 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. |