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granite-4.2-8B

webAI-Official

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A 1.7B reasoning model for **formal logic** tasks, built from `HuggingFaceTB/SmolLM2-1.7B-Instruct` through LoRA supervised fine-tuning, checkpoint fusion, WiSE-FT weight interpolation, and entropy-weighted GRPO reinforcement learning. It raises in-domain formal-logic performance by **+49% relative** over its base model (macro gate 0.263 → 0.393), and on the strict, no-partial-credit reading of Track A it is the strongest model we have measured at any size — ahead of Qwen3-8B and of a 26B Gemma-4 MoE. Its larger sibling, **TwIL-LM3** (3B, from SmolLM3), trades a smaller in-domain gain for strictly better held-out retention. If you care about not regressing on general benchmarks, prefer that one.

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TwIL-LM2

webAI-Official

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TwIL-LM3

webAI-Official

A 3B reasoning model for **formal logic** tasks, built from `HuggingFaceTB/SmolLM3-3B` through LoRA supervised fine-tuning, checkpoint fusion, WiSE-FT weight interpolation, and entropy-weighted GRPO reinforcement learning. It improves in-domain formal-logic performance by **+26% relative** over its base model (macro gate 0.336 → 0.422) **and improves held-out benchmark performance at the same time** (+0.022 core average). It is the only arm in this project that gains on both tracks, which is why it is the recommended release of the pair. * **Gains on both tracks at once** — the only arm in this project that does. In-domain macro gate 0.336 → 0.422, and the held-out 10-dataset macro 0.7193 → 0.7339 rather than the usual collapse that follows task-specific fine-tuning. * **Beats every arm up to and including LFM2.5-8B-A1B** — roughly three times its parameter count — on all six Track A objective lanes and all four summary rows, not on average alone. * **Competitive with 8B on strict scoring.** On strict-7, which gives no loose-match credit anywhere, it sits 0.012 behind Qwen3-8B (0.1971 against 0.2093) at 2.6x fewer parameters, and ahead of it on Lean formalisation (token-F1 0.5869 against 0.4022) and semantic parsing (0.4416 against 0.4257). * **Structured formal output.** Tuned for the objects rather than the prose: FOL translation, entailment labels, semantic parses, Lean statements and Lean proof critique. * **The most efficient arm measured, at any scale.** 482-token Track B generations and 32.9 completed answers per second — about eight times gpt-oss-120b's rate — because it answers short rather than because it decodes unusually fast. * **Lowest maths-corpus perplexity of any released arm in the table** (3.8229), including Qwen3-8B at 4.0083. * **Runs anywhere.** 3.08B parameters in bf16, with Q4\_K\_M GGUF at 1.78 GiB for CPU or 4 GB of VRAM.

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TwIL-LM3-Pro

webAI-Official

A 3.66B reasoning model for **formal logic** tasks, built from `ibm-granite/granite-4.2-3b` through LoRA supervised fine-tuning, checkpoint fusion, WiSE-FT weight interpolation, and entropy-weighted GRPO reinforcement learning. It improves in-domain formal-logic performance by **+28% relative** over its base model (macro gate 0.431 → 0.554) **while holding held-out benchmark performance** — the 10-dataset macro is level with the base (0.7942 → 0.7901) and the 14-dataset macro improves slightly (0.7332 → 0.7425). On the same harness and sampled rows it reaches the highest Track A macro gate, strict-7 and six-lane average of any arm in the tables below for which each can be computed, including Qwen3-8B, Qwen3.5-4B, VibeThinker-3B and gpt-oss-120b (the 120B has no gate or strict-7 value). *VibeThinker-webAI-trained is the public VibeThinker-3B after the same post-training pipeline (SLERP, d = 0.5, t = 0.5); see Against the tuned VibeThinker-3B. It has no strict-7 or six-lane-average value, so those bars show n/a.*

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