DeployBench is a new benchmark of 51 research-artifact deployment tasks where four LLMs with OpenHands achieve 7.8-51% pass rates, with failures mostly from agents stopping after weaker self-checks than the paper requires.
CodeCriticBench: A holistic code critique benchmark for large language models
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Reasoning in large output spaces proceeds via shortlisting then fine-grained reasoning; this characterization enables a mechanistic distillation strategy that outperforms standard distillation.
This survey organizes RL for LLM multi-agent systems into reward families, credit units, and five orchestration sub-decisions, notes the absence of explicit stopping-decision training in its paper pool, and releases a tagged corpus.
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DeployBench: Benchmarking LLM Agents for Research Artifact Deployment
DeployBench is a new benchmark of 51 research-artifact deployment tasks where four LLMs with OpenHands achieve 7.8-51% pass rates, with failures mostly from agents stopping after weaker self-checks than the paper requires.
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Characterize Then Distill: Mechanistic Reasoning in Large Output Spaces
Reasoning in large output spaces proceeds via shortlisting then fine-grained reasoning; this characterization enables a mechanistic distillation strategy that outperforms standard distillation.
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Reinforcement Learning for LLM-based Multi-Agent Systems through Orchestration Traces
This survey organizes RL for LLM multi-agent systems into reward families, credit units, and five orchestration sub-decisions, notes the absence of explicit stopping-decision training in its paper pool, and releases a tagged corpus.