arXiv AI
SWE-Ladder: Learning to Localize, Reproduce, and Patch via Progressive Privileged Self-Distillation
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arXiv Machine Learning
Oct 5
Learning from Evolving Errors: Adaptive Iterative Repair for On-Policy Distillation
arXiv:2610.02700v1 Announce Type: new Abstract: On-policy self-distillation (OPSD) supplies dense token-level feedback on trajectories sampled from the student's own policy, a richer training signal...
arXiv AI
Aug 26
PROOF-Gen: From Optimized Data to Better Distillation
PROOF-Gen is a method that improves distillation of tool‑calling models by recovering successful trajectories from teacher failures. It uses per‑scenario prompt optimization to generate corrective guidance that steers the teacher to a passing trajectory, then removes this guidance before training so the student learns from clean demonstrations. On τ2‑bench, PROOF-Gen recovers 93% of failed scenarios, boosting Qwen3‑4B‑Instruct‑2507’s Pass^1 from 0.132 to 0.529 and improving Gemma 4 E4B‑it by 7.2pp on BFCL v4 multi‑turn, while also raising deployed on‑device model performance by up to 5.0pp across response‑quality metrics.
arXiv Machine Learning
Oct 7
FC-SWE: Failure-Conditioned RL for Long-Horizon Software Engineering Agents
arXiv:2610.07898v1 Announce Type: new Abstract: Repository-level software engineering (SWE) is a challenging long-horizon setting: agents must reason over extended interactions, use tools, and adapt...
arXiv AI
Sep 10
ExecCritic: Learn to Test, Test to Improve for Coding Agents
arXiv:2609.09133v1 Announce Type: new Abstract: Execution feedback can guide coding agents toward correct repository repairs, but only when the tests capture the behavior requested by the issue. Agen...
arXiv Computation and Language
Oct 16
OPRD: On-Policy Reminder Distillation for Long-Horizon Coding Agents
The paper introduces On-Policy Reminder Distillation (OPRD), a method that enhances long-horizon coding agents by combining multi-scope reminder supervision with outcome-based routing. OPRD uses successful rollouts as autoregressive self-imitation targets and failed rollouts for reminder-conditioned reverse-KL supervision from a teacher that re-evaluates student prefixes. The approach improves task resolution rates by 11.3 points and cuts error events by 50.2% on several SWE-bench benchmarks.
arXiv AI
Oct 1
Agent Error Dataset: Scaling 50,000 Error--Diagnosis Pairs for Failure Analysis and Error-Aware Post-Training
The Agent Error Dataset (AED) presents 50,228 error–diagnosis pairs collected from 9,961 source tasks across 33 environments, 19 harness families, and 23 policy models in text‑based agent systems. A five‑stage Agentic Error‑to‑Training (AET) pipeline generates diagnoses and proposed corrections, verifies them against recorded evidence, and creates separate training views for diagnosis and actor recovery. Experiments show that first‑proposal corrections improve verifier pass rates from 18.4% to 51.1%, and fine‑tuning with full‑diagnosis data raises Qwen3‑8B’s exact‑step agreement from 47.2% to 63.6% on a holdout set.