arXiv AI
Aug 6
COMPAS: Difficulty-Aware Joint Search for Optimizing Code Generation
arXiv:2608. 04336v1 Announce Type: cross Abstract: Code generation systems make each LLM call with a model, a prompt, and decoding settings.
Reinforcement Learning for Code Optimization
arXiv:2607. 25970v1 Announce Type: cross Abstract: RL for code correctness is now established: have the model generate a program, run it against hidden test cases, and reward solutions that pass.
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arXiv Machine Learning
Aug 26
Mitigating Exploration Bias in RL for Multi-Instruction Following
arXiv:2608.23830v1 Announce Type: cross Abstract: RL has emerged as a powerful paradigm for enhancing the instruction following capabilities of LLMs. While existing training recipes achieve substanti...
arXiv Machine Learning
Jul 31
RLPF: Reinforcement Learning from Performance Feedback for Code Generation
arXiv:2607. 27271v1 Announce Type: new Abstract: Code models are increasingly trained with execution feedback, but most training signals still stop at correctness.
arXiv AI
4d ago
Teaching LLMs to Generate Challenging MILP Instances via Solver Feedback
arXiv:2609.37356v1 Announce Type: new Abstract: Generating optimization instances that are both feasible and computationally challenging is crucial for benchmarking solvers and training learning-base...
arXiv AI
Jun 18
Breaking the Solver Bottleneck: Training Task Generators at the Learnable Frontier
arXiv:2606. 18284v1 Announce Type: cross Abstract: The limiting resource for training agents via reinforcement learning (RL) is increasingly frontier task supply: valid, solvable tasks just difficult enough to train the current model.
arXiv Machine Learning
Jun 26
Reinforcement Learning without Ground-Truth Solutions can Improve LLMs
arXiv:2606. 27369v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) for training LLMs typically rely on ground-truth answers to assign rewards, limiting their applicability to tasks where the ground-truth solution is unknown.
arXiv AI
Jun 3
Synthesize and Reward -- Reinforcement Learning for Multi-Step Tool Use in Live Environments
arXiv:2606. 03892v1 Announce Type: cross Abstract: Training LLMs to orchestrate multi-step tool calls is held back by three coupled obstacles: realistic stateful execution environments are costly to build, synthetic training queries are often detached from the server's actual state (so the generated tool calls fail to execute), and recall-based RL rewards incentivize verbose tool-calling patterns.
arXiv Machine Learning
Jul 14
SCOPE-RL: Optimizing Reasoning Paths Before and After Success
arXiv:2607. 11506v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) optimizes LLMs using sparse verifiable final-answer rewards.
arXiv AI
Sep 15
Learning to Solve Hard Problems in RL for LLMs by Never Giving Up
arXiv:2609.13443v1 Announce Type: cross Abstract: We demonstrate that training LLMs with RL does not improve performance equally across a dataset. RL shows large improvements on easy problems that an...
arXiv AI
Jun 11
When Does Deep RL Beat Calibrated Baselines? A Benchmark Study on Adaptive Resource Control
arXiv:2605. 26418v2 Announce Type: replace-cross Abstract: A properly calibrated rule-based autoscaler can beat every one of six mainstream deep reinforcement learning (DRL) algorithms on cost across every workload we test - so when, if ever, does DRL actually help?
arXiv Machine Learning
Sep 10
DataFlex-RL: An Evaluation Platform for RLVR Data Policies
arXiv:2609.06107v1 Announce Type: new Abstract: Data policies for reinforcement learning with verifiable rewards (RLVR) determine which rollouts are used, how strongly they are weighted, and which do...
arXiv AI
Jun 3
Aletheia: What Makes RLVR For Code Verifiers Tick?
arXiv:2601. 12186v3 Announce Type: replace-cross Abstract: Multi-domain thinking verifiers trained via Reinforcement Learning with Verifiable Rewards (RLVR) are a cornerstone of modern post-training.