Audit of GSO, SWE-Perf and SWE-fficiency reveals that reference patches satisfy validity rules across machines for only 39/102, 11/140 and 411/498 tasks respectively, public submissions beat references on 85.3% of replay-valid tasks, and scoring rules cause ranking disagreements.
Reinforcement learning foundations for deep research systems: A survey
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ERL trains LLMs to erase faulty reasoning steps and regenerate them in place, yielding gains of up to 8.48% EM on multi-hop QA benchmarks like HotpotQA.
The paper reviews conceptual foundations, methodological innovations, effective designs, critical challenges, and future directions for LLM-based Agentic Reinforcement Learning.
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