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IDEALLab/Qwen2.5-Coder-14B-Instruct-GRPO-SDS-NoHypothesize-step90-seed101
Qwen2.5-Coder-14B-Instruct-GRPO-SDS-NoHypothesize-step90-seed101 is a reinforcement learning model from IDEALLab. Use it for the reinforcement learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.
This is the frozen step-90 policy for seed 101 from the matched training-time prompt ablation in "Beyond Inference-Time Search: Reinforcement Learning Synthesizes Reusable Solvers." The intervention removes exactly th…
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From the Hugging Face model README
This is the frozen step-90 policy for seed 101 from the matched training-time
prompt ablation in "Beyond Inference-Time Search: Reinforcement Learning
Synthesizes Reusable Solvers." The intervention removes exactly the
Hypothesize instruction from the otherwise matched Hero configuration.
Public code commit: https://github.com/IDEALLab/neural-solver-synthesis/commit/07a798e7d7eca736cd1ef13a15209d402d401ef6
Final aggregate evidence and per-seed results: https://huggingface.co/datasets/IDEALLab/Neural-Solver-Synthesis-Final-Evidence-v1
At step 90, removing the instruction lowers seed-macro feasibility and worsens certified gap, with strong heterogeneity across seeds. Exploratory later checkpoints recover non-monotonically. The evidence therefore supports a sample-efficiency and stability interpretation, not inaccessible knowledge or universal prompt necessity. Only one prompt instruction is isolated.
files.sha256.json records SHA-256 hashes of every inference file uploaded from
the frozen checkpoint. Optimizer, scheduler, RNG, and trainer state are excluded.