Generating Constructive Feedback on Stories via Reinforcement Learning

While LLMs hold significant potential to transform scientific research, we advocate for their use to augment and empower researchers rather than to automate research without human oversight. To this end, we study constructive feedback generation, the task of producing targeted, actionable feedback that helps authors improve both their research and its presentation. In this work, we operationalize the effectiveness of feedback along two author-centric axes-validity and author action. We first curate GoodPoint-ICLR, a dataset of 19K ICLR papers with reviewer feedback annotated along both dimensions using author responses. Building on this, we introduce GoodPoint, a training recipe that leverages success signals from author responses through fine-tuning on valid and actionable feedback, together with preference optimization on both real and synthetic preference pairs. Our evaluation on a benchmark of 1.2K ICLR papers shows that a GoodPoint-trained Qwen3-8B improves the predicted success rate by 83.7% over the base model and sets a new state-of-the-art among LLMs of similar size in feedback matching on a golden human feedback set, even surpassing Gemini-3-flash in precision. We further validate these findings through an expert human study, demonstrating that GoodPoint consistently delivers higher practical value as perceived by authors.

Training models for open-ended writing requires diverse prompts and sufficiently precise specifications of response quality. Constructing such specifications at scale entails recovering task requirements, consolidating overlapping criteria, and defining observable differences between performance levels. We introduce Writer-R1, a writing training pipeline built around Inductive Rubric Generation (IRG), which integrates these operations into an automated rubric construction procedure. The pipeline expands an open writing corpus and uses the resulting prompt-specific rubrics throughout supervised training and reinforcement learning (RL). Each rubric is concatenated with the user request and also defines the reward criteria, making the requirements used for assessment available to the writer before generation. A compact model, RubricGen-4B, learns to supply rubrics for new requests. Writer-R1 improves WritingBench scores by 0.182 and 0.158 over the 4B and 8B backbones, respectively, with gains also on HelloBench. Training ablations favor IRG over direct rubric generation, and further analyses support rubric transfer and the robustness of writer improvements across evaluators. These results support using inductively mined rubrics as both generation guidance and reward specifications for open-ended writing.

We investigate whether state-of-the-art large language models (LLMs) generate feedback that reflects the pedagogical practices of expert teachers in terms of feedback focus and adaptivity. Previous evaluation efforts have examined feedback characteristics, its impact on learning, and its target, yet the focus of feedback and its adaptivity remains largely overlooked. To bridge this gap, we adopt and refine Narciss's taxonomy into seven feedback focus types to annotate teacher and LLM-generated feedback across three university writing courses. We release FeedType, a benchmark containing annotated teacher and LLM feedback from six LLMs under three prompting strategies. We assess the coverage and distribution of feedback focus types, and examine whether LLMs adapt their feedback across draft stages and student performance levels as an expert instructor does. Our findings show that while most LLMs cover most feedback focus types, they fail to reflect teacher feedback distributions and show varying levels of adaptivity, with none matching the teachers' adaptive behavior. We believe FeedType will support future research on pedagogical alignment in LLM feedback generation.