TL;DR
This work proposes using reinforcement learning to synthesize human-readable Python programs that replicate an LLM’s input–output behavior, providing behavioral rather than internally faithful explanations.
This work proposes RLPF, reinforcement learning from performance feedback, which turns execution outcomes into a staged reward, and suggests that code agents can be trained not only to pass tests, but also to optimize the programs they write.
This work proposes a novel Reinforcement Learning from AI Feedback (RLAIF) framework, finding that specifications can be iteratively refined by leveraging feedback from the LLM's own outputs, creating a self-improving loop.
It is shown that the residual streams of language models under coding agents linearly encode properties of the evolving program: a logistic-regression probe on hidden states is able to decode whether the current code parses, passes its test suite, reduces the number of failing tests, and introduces regressions.
River, a simple training recipe that improves reward quality by filtering low-quality environments and augmenting outcome rewards with process-level behavior regularization is proposed, which achieves the best performance among evaluated open-source RL-trained 8B models across four terminal-agent benchmarks.
DHRCL decomposes feedback into syntax validation, execution success, unit-test pass rate, and AST-based structural similarity, and organizes these signals through a three-stage Syntax, Execution, Pass&Structural curriculum, and introduces stage-aware probability-based token credit redistribution.
Self-Supervised Skill Optimization (SSO) is introduced, a comparative framework that learns a reusable skill from unlabeled task instances alone and outperforms existing GT-free prompt optimizers on both closed-ended and open-ended tasks.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
