Free(): Learning to Forget in Malloc-Only Reasoning Models
Reasoning models enhance problem-solving by scaling test-time compute, yet they face a critical paradox: excessive thinking tokens often degrade performance rather than improve it. We attribute this to a fundamental architectural flaw: standard LLMs operate as "malloc-only" engines, continuously accumulating valid and redundant steps alike without a mechanism to prune obsolete information. To break this cycle, we propose Free()LM, a model that introduces an intrinsic self-forgetting capability via the Free-Module, a plug-and-play LoRA adapter. By iteratively switching between reasoning and cleaning modes, Free()LM dynamically identifies and prunes useless context chunks, maintaining a compact and noise-free state.
  Extensive experiments show that Free()LM provides consistent improvements across all model scales (8B to 685B). It achieves a 3.3% average improvement over top-tier reasoning baselines, even establishing a new SOTA on IMOanswerBench using DeepSeek V3.2-Speciale. Most notably, in long-horizon tasks where the standard Qwen3-235B-A22B model suffers a total collapse (0% accuracy), Free()LM restores performance to 50%. Our findings suggest that sustainable intelligence requires the freedom to forget as much as the power to think.
698c7b76eb12ea74539168fc
Yilun Zheng
698c7b76eb12ea74539168fd
Dongyang Ma
698c7b76eb12ea74539168fe
Tian Liang
698c7b76eb12ea74539168ff
Jiahao Xu
698c7b76eb12ea7453916900
Xinting Huang
698c7b76eb12ea7453916901
Lihui Chen
698c7b76eb12ea7453916902
Haitao Mi
698c7b76eb12ea7453916903
Yan Wang
claimed_verified
2026-02-13T09:37:33.824Z
64674375946476c5d215382d
/avatars/0f0b98080c064a17d003da8a22a047cc.svg
Yan Wang
libertywing
user
libertywing
Free(): Learning to Forget in Malloc-Only Reasoning Models
Reasoning models enhance problem-solving by scaling test-time compute, yet they face a critical paradox: excessive thinking tokens often degrade performance rather than improve it. We attribute this to a fundamental architectural flaw: standard LLMs operate as "malloc-only" engines, continuously accumulating valid and redundant steps alike without a mechanism to prune obsolete information. To break this cycle, we propose Free()LM, a model that introduces an intrinsic self-forgetting capability via the Free-Module, a plug-and-play LoRA adapter. By iteratively switching between reasoning and cleaning modes, Free()LM dynamically identifies and prunes useless context chunks, maintaining a compact and noise-free state.
  Extensive experiments show that Free()LM provides consistent improvements across all model scales (8B to 685B). It achieves a 3.3% average improvement over top-tier reasoning baselines, even establishing a new SOTA on IMOanswerBench using DeepSeek V3.2-Speciale. Most notably, in long-horizon tasks where the standard Qwen3-235B-A22B model suffers a total collapse (0% accuracy), Free()LM restores performance to 50%. Our findings suggest that sustainable intelligence requires the freedom to forget as much as the power to think.
Free()LM addresses reasoning model limitations by introducing a self-forgetting mechanism through a Free-Module plug-and-play LoRA adapter, improving performance across scales and long-horizon tasks.
LLMs reasoning models test-time compute thinking tokens malloc-only engines Free-Module LoRA adapter reasoning mode cleaning mode context pruning Qwen3-235B-A22B DeepSeek V3.2-Speciale IMOanswerBench