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Model: groc/recursive-sat-qwen2.5-1.5b
Source: Original Platform

README.md

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+---
+base_model: Qwen/Qwen2.5-1.5B-Instruct
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- paper-model
+- recursive-reasoning
+- sat
+- qwen2.5
+- transformers
+datasets:
+- LLM4Code/SATBench
+license: mit
+---
+
+# recursive-sat-qwen2.5-1.5b
+
+This is a paper model: the `REC-3` release artifact from a paper-aligned replication of recursive SAT reasoning at 1.5B scale.
+
+It is a supervised fine-tune of `Qwen/Qwen2.5-1.5B-Instruct` trained on recursive SAT traces derived from SATBench with explicit `<call>` / `<return>` structure. The goal is research replication and analysis, not general-purpose production use.
+
+## What This Model Is
+
+- Base model: `Qwen/Qwen2.5-1.5B-Instruct`
+- Release artifact: `results/runs/REC-3/published_model`
+- Training run: `REC-3`
+- Seed: `303`
+- Config: `configs/rec_seed303.yaml`
+- Dataset source: `LLM4Code/SATBench`
+- Task: SAT / UNSAT classification via recursive trace supervision
+
+## Why REC-3
+
+`REC-1` and `REC-3` tie on mean accuracy, but `REC-3` is the cleaner release candidate on end-to-end behavior:
+
+- Mean accuracy: `45.33%`
+- Easy: `39.0%`
+- Medium: `54.0%`
+- Hard: `43.0%`
+- Parse failure rate: `7.0%`
+- Valid trace rate: `99.0%`
+
+Compared with `REC-1`, `REC-3` keeps the same mean accuracy while reducing parse failure (`7.0%` vs `8.33%`), improving hard accuracy (`43.0%` vs `42.0%`), and slightly improving valid trace rate (`99.0%` vs `98.33%`).
+
+## Important Caveat
+
+This is a paper model, not a claim of robust general recursive reasoning.
+
+The underlying paper draft treats the result as a qualified replication:
+
+- recursive SFT improves end-to-end SATBench accuracy over raw direct prompting
+- the strongest gain is on medium-difficulty SAT instances
+- absolute performance remains far below the 3B source-paper result
+- recursion behavior is still shallow overall
+
+Use this release as a research artifact tied to the experiment, metrics, and discussion in the paper repo.
+
+## Training Summary
+
+- Objective: `recursive_sft`
+- Train examples: `74,827`
+- Validation examples: `619`
+- Global step: `46,770`
+- Best checkpoint: `checkpoint-9354`
+- Accelerator used for the main run: `cuda`
+
+## Evaluation Summary
+
+Main held-out evaluation uses `100` examples each from SATBench easy, medium, and hard buckets.
+
+Baseline vs released model:
+
+- Base direct prompt mean accuracy: `37.33%`
+- `REC-3` mean accuracy: `45.33%`
+- Absolute gain: `+8.0 points`
+- Base parse failure rate: `28.67%`
+- `REC-3` parse failure rate: `7.0%`
+
+## Prompt Format
+
+The model was trained on recursive traces using:
+
+- `<call> ... </call>` for subproblem decomposition
+- `<return> ... </return>` for compact returned answers
+
+It is best treated as a specialized research model for this protocolized SAT setting.
+
+## Files In This Release
+
+- `model.safetensors`
+- `config.json`
+- `generation_config.json`
+- `tokenizer.json`
+- `tokenizer_config.json`
+- `chat_template.jinja`
+- `export_metadata.json`
+
+## Intended Use
+
+- paper artifact release
+- replication reference
+- SAT recursive-trace evaluation
+- qualitative inspection of recursive protocol behavior
+
+## Out Of Scope
+
+- production reasoning system
+- general mathematical reasoning benchmark model
+- safety-critical use
+- claims beyond the SATBench replication setting