penfever/ablation_exploration_in_rl

Reinforcement Learning Improves Agentic Software Engineering An ablation study of reinforcement-learning (RL) fine-tuning for agentic software-engineering (SWE) models. Starting from an 8B SFT model, we fine-tune with RL across ~20 configurations — varying the objective, loss normalization, sampling, and training dataset — and evaluate each on agentic SWE benchmarks. Result RL reliably and substantially improves agentic SWE performance, and the improvement is… See the full description on the dataset page: https://huggingface.co/datasets/penfever/ablation_exploration_in_rl.

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