1. Introduction
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.
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.
2. Key Capabilities
- 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.
3. Performance
"—" 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.
4. Efficiency in Practice
Same problem, same correct solution, radically different think length.
Task: write separate_paren_groups, a function that splits a string of parentheses into its top-level balanced groups.
Qwen3.6-27B think: 6,539 tokens. Starts like this and keeps going for pages:
LRM-3.2 think: 33 tokens. The whole thing:
Same answer quality. 198x less think.
5. Training
LRM-3.2 is fine-tuned from Qwen3.6-27B on the grug-think and 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.
LRM-3.2 is directly inspired by 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.
6. Architecture
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.
Orion Research - 2026