| --- |
| license: apache-2.0 |
| base_model: |
| - Qwen/Qwen3.6-27B |
| datasets: |
| - ProCreations/grug-think |
| - ProCreations/grug-think-v3-10k |
| pipeline_tag: text-generation |
| --- |
| |
| ## 1. Introduction |
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| We're introducing **LRM-3.2**, a reasoning model built around a single idea: thinking should be *fast, direct, and dense* — not padded. LRM-3.2 keeps the full depth of chain-of-thought reasoning while stripping out the narrative scaffolding that most models use to fill space. |
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| Same reasoning. Same depth. Way fewer tokens. LRM-3.2 throws the grammar padding in the fire and keeps all the brain meat. The final answer still comes out in normal, full-quality English — the compressed voice lives only inside the thinking process. |
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| ## 2. Key Capabilities |
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| - **Adaptive Depth:** Thinking length scales with task difficulty, not with habit. Easy problems get a one-line think; hard problems still get full, structured deliberation. |
| - **Unchanged Output Quality:** Compression happens exclusively in the reasoning trace. Final answers remain complete, natural, and equivalent in quality to verbose-thinking models. |
| - **Fast, Direct Inference:** Dramatically shorter think blocks translate directly into lower latency and lower token spend per response, without a distillation-style drop in capability. |
| - **Reliable Under Long Sessions:** Reasoning stays dense and on-task across extended agentic and multi-step work, rather than drifting into repetitive verbal habits. |
|
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| ## 3. Performance |
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|
| <table> |
| <tr> |
| <th style="background: rgba(128,128,128,0.1); text-align: center;"> </th> |
| <th style="background: rgba(128,128,128,0.1); text-align: center;">LRM-3.2</th> |
| <th style="background: rgba(128,128,128,0.1); text-align: center;">Qwen3.6-27B</th> |
| </tr> |
| <tr> |
| <td align="center" colspan="3" style="background: linear-gradient(90deg, rgba(124,58,237,0.45) 0%, rgba(99,102,241,0.42) 50%, rgba(59,130,246,0.45) 100%); font-weight: bold; height:32px; padding-top:2px; padding-bottom:2px;"><i>Reasoning & Coding</i></td> |
| </tr> |
| <tr> |
| <td align="center">GSM8K</td> |
| <td align="center"><b>95.8</b></td> |
| <td align="center">—</td> |
| </tr> |
| <tr> |
| <td align="center">HumanEval</td> |
| <td align="center"><b>86.9</b></td> |
| <td align="center">—</td> |
| </tr> |
| </table> |
| |
| *"—" indicates a score not yet measured on this harness. Both benchmarks reflect matched-quality answers between LRM-3.2 and its base model — the difference lives in the think trace, not the final result.* |
|
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| ## 4. Efficiency in Practice |
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| Same problem, same correct solution, radically different think length. |
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| Task: write `separate_paren_groups`, a function that splits a string of parentheses into its top-level balanced groups. |
|
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| **Qwen3.6-27B think: 6,539 tokens.** Starts like this and keeps going for pages: |
|
|
| <div style="background: rgba(124,58,237,0.10); border: 1px solid rgba(124,58,237,0.35); border-left: 4px solid #7C3AED; border-radius: 10px; padding: 12px 16px; margin: 8px 0;"> |
| The user wants a Python function <code>separate_paren_groups</code> that takes a string of parentheses and spaces, and returns a list of strings. Each string in the list should represent a balanced group of parentheses that is not nested within another group. Spaces should be ignored... |
| </div> |
|
|
| **LRM-3.2 think: 33 tokens. The whole thing:** |
|
|
| <div style="background: rgba(124,58,237,0.10); border: 1px solid rgba(124,58,237,0.35); border-left: 4px solid #7C3AED; border-radius: 10px; padding: 12px 16px; margin: 8px 0;"> |
| Strip spaces. Scan chars; depth counts open parens. When depth becomes 0 after a close, current group finished; append and reset. Empty input -> []. |
| </div> |
|
|
| Same answer quality. 198x less think. |
|
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| ## 5. Training |
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| LRM-3.2 is fine-tuned from **Qwen3.6-27B** on the [grug-think](https://huggingface.co/datasets/ProCreations/grug-think) and [grug-think-v3-10k](https://huggingface.co/datasets/ProCreations/grug-think-v3-10k) datasets, applying a think-only loss on trajectory data so the compressed reasoning style is learned without touching final-answer quality. |
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| LRM-3.2 is directly inspired by [ProCreations/grug-27b](https://huggingface.co/ProCreations/grug-27b), which pioneered this padding-free thinking approach on the same base model family. LRM-3.2 adapts that approach under the OrionLLM naming and evaluation pipeline. |
|
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| ## 6. Architecture |
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| LRM-3.2 is built on **Qwen3.6-27B**, a 27B-parameter dense model, fine-tuned to compress the reasoning trace while leaving final-answer generation untouched. No changes are made to the base tokenizer, context length, or output formatting — only the internal thinking style is altered. |
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| --- |
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| <div align="center"> |
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| **Orion Research - 2026** |
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| </div> |
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