TL;DR
This work studies two prompt-distribution interventions while keeping the SFT initialization, verifier, and RLOO objective fixed, and compares uniform RLOO, a static curriculum, and an adaptive curriculum.
This work casts per-step rollout collection as a budget-constrained sequential allocation problem and introduces SARA (Sequential Adaptive Rollout Allocation), a two-threshold, SPRT-style rule that commits effective groups, abandons saturated ones after a short probe, and reallocates the freed budget to fresh prompts,...
This work makes execution time learnable through three stages: how code is tested, by building DMC-Optim with large optimization tests and a calibrated sandbox; how speed is turned into reward, by composing correctness and speed in the RL environment and using an offline simulator to predict the most promising configur...
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.
It is shown that on-policy reinforcement learning with verifiable rewards (RLVR) can improve the current objective while making successful behaviors for later objectives too rare to sample and reinforce, and effective rewardable support is defined as successful trajectories reachable within a fixed rollout budget.
This work introduces SpecRoll, a speculative rollout engine that preserves the target model's sampling distribution while adapting at two timescales, and combines concurrency-aware sparse-tree verification and exact target verification, leaving the target rollout distribution and GRPO objective unchanged.
AdaKP is an online selector that re-chooses each problem's KP subset over the course of RL training, an entropy proxy that scores a KP by the reduction in next-token entropy it induces in a single inexpensive forward pass, with a provable bound on its truncation bias.
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