Agentic Coding19d ago
Ending Coding Agent Reward Hacking: CMU's Weak-to-Strong Verifier Hardening Adopted by Terminal-Bench, While Recursive Environment Scaling Boosts SFT by up to 14.6 Points
Tackling reward hacking and benchmark saturation in coding agent environments, CMU researchers introduce Scalable Weak-to-Strong Verifier Hardening (arXiv:2610.15924)—combining an isolated verifier sandbox with an iterative hacker-fixer loop that slashes exploit success across Terminal-Bench, KernelBench, and SETA, and is now officially adopted by Terminal-Bench. Complementing this, Recursive Environment Scaling (RES, arXiv:2610.16397) recursively mutates execution dependencies across 294 tasks—exposing a 57.14-77.14 point drop on Claw-Eval-Live while boosting equal-budget SFT performance by 7.00-14.61 percentage points across all nine model-benchmark pairs.
Coding20d ago
PreciseCoder: Augmenting Unit-Test RL with Localized Semantic Rewards Cuts Code Edits from 7–15 Lines to 3 and Boosts GPT-5.6 Sol Precision
While reinforcement learning from unit tests raises code pass rates, researchers discover it inadvertently teaches models to over-edit and hallucinate bugs in already-correct code. PreciseCoder augments unit-test rewards with a semantically verified localized edit signal. Across 4B, 9B, and 27B models, PreciseCoder slashes average edit lines from 7–15 down to 3 at equal pass rates, generalizes from single-line training to repository-level repair, and transfers 9B reasoning traces to boost GPT-5.6 Sol and Qwen3.8-Max edit precision by 4.6–12.4 points without retraining.
Benchmark38d ago
AgentWorld: Benchmarking Long-Horizon Collaboration of Multi-Agent LLMs Across 50+ Rounds
Overcoming the limits of traditional multi-agent benchmarks that only test short-horizon (<20 steps) or competitive settings, UPenn researchers released AgentWorld. Spanning 50+ interaction rounds in an MMORPG sandbox across 100 human-annotated tasks with 3-20 asymmetric black-box agents, it introduces Causal Collaboration Effectiveness (CCE). Even frontier models achieve only 52.0% task success, exposing severe communication breakdown and shared plan decay.
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