Rubric-to-Code Credit Assignment for Reinforcement Learning
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
The Flow has not summarised this story yet — read it at arXiv AI.
BoostAPR is a three-stage framework that improves automated program repair by using execution-grounded reinforcement learning with dual reward models. The approach first fine‑tunes a model on execution‑verified demonstrations, then trains a sequence‑level assessor and a line‑level credit allocator from execution outcomes, and finally applies PPO optimization where the line‑level model redistributes rewards to critical edit regions. Evaluated on SWE‑Gym and four benchmarks, BoostAPR achieves significant gains, including 40.7% on SWE‑bench Verified and 95.0% on QuixBugs, demonstrating strong cross‑language generalization.
arXiv:2608. 16156v1 Announce Type: new Abstract: Long-horizon large language model (LLM) agents are typically optimized with sparse terminal outcomes, making fine-grained credit assignment across multi-step interactions difficult.
Long-horizon large language model (LLM) agents are typically optimized with sparse terminal outcomes, making fine-grained credit assignment across multi-step interactions difficult. Existing approache...
arXiv:2607. 27271v1 Announce Type: new Abstract: Code models are increasingly trained with execution feedback, but most training signals still stop at correctness.
arXiv:2608. 07147v1 Announce Type: new Abstract: Reinforcement learning with Verifiable Reward (RLVR) has emerged as a powerful paradigm for training coding agents, where the execution feedback from compilation and tests provides objective verification.
SLCA-GRPO addresses cross‑segment credit misattribution in tool‑calling reinforcement learning by introducing Segment‑Locked Credit Assignment (SLCA), which separates advantage estimation for tool‑invocation tokens and natural‑language summary tokens. The method leverages a Schema‑Guided LLM Simulator (SGLS) for scalable training and Hierarchical Rewards (HierR) to route execution and preference advantages appropriately. Experiments on a 7B backbone show that SLCA‑GRPO outperforms baseline methods, improving in‑domain accuracy by 2.53 pp, the Berkeley Function‑Calling Leaderboard by 1.36 pp, and $ au^2$‑Bench by 9.15 pp while reducing tool redundancy and costs.