TL;DR
This project asks whether online reinforcement learning can improve an already supervised-finetuned Countdown model, and whether dynamically choosing problem difficulty can make the reward signal more useful.
Cliff, a reward shaping strategy that utilizes an off-the-shelf LLM as a teacher to identify the first mistake in each rollout, is proposed and established as a simple, general and effective approach for improving RLVR with richer, fine-grained supervision.
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 uses classic instruction tuning, supervised fine-tuning without reasoning traces, on the RLM to improve RLM performance in both verifiable and hard-to-verify domains, including coding and text summarization, while preserving RLM capabilities across other domains.
This work introduces SPEAR (Symbolic Process Evaluation and Alignment Reward), a training-free and plug-and-play process reward method for sequence-level on-policy distillation that effectively bridges the reasoning gap between student and teacher models via sequence-level distillation with efficient dense process rewa...
Results support a qualified internalized-search reading: under the recipe the authors test, much of the measured RL gain corresponds to a change in sampling efficiency toward operating points the base model can already reach under search.
PLVR (Program Learning with Verifiable Rewards): a post training method that learns such programs directly from input-output examples and is released with the symbolic backpropagation library and a conformance checker so the method can be applied to primitive libraries other than the authors' own.
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