DAgger-style training with turn-level policy interpolation raises 4B and 8B LLM agents to 27.3% and 29.8% on SWE-bench Verified, beating several larger published systems.
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Hindsight Hint Distillation synthesizes hindsight hints from failed rollouts to scaffold successful on-policy trajectories from CoT-free QA data, then self-distills them to improve SWE agent performance by 8% on SWE-bench Verified with strong out-of-distribution generalization.
citing papers explorer
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Revisiting DAgger in the Era of LLM-Agents
DAgger-style training with turn-level policy interpolation raises 4B and 8B LLM agents to 27.3% and 29.8% on SWE-bench Verified, beating several larger published systems.
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Hindsight Hint Distillation: Scaffolded Reasoning for SWE Agents from CoT-free Answers
Hindsight Hint Distillation synthesizes hindsight hints from failed rollouts to scaffold successful on-policy trajectories from CoT-free QA data, then self-distills them to improve SWE agent performance by 8% on SWE-bench Verified with strong out-of-distribution generalization.