RiVER framework enables RL training of LLMs on tasks without ground-truth solutions
Researchers introduce RiVER (Ranking-induced VERifiable framework), a reinforcement learning approach that trains LLMs on score-based optimization tasks using deterministic execution feedback as continuous rewards, without requiring ground-truth answers. The method addresses two failure modes in group-relative RL with continuous rewards—scale dominance and frequency dominance—via calibrated, instance-wise reward shaping. Applied to Qwen3-8B and GLM-Z1-9B-0414 on competitive programming tasks, RiVER improves ALE rating rank by ~9% and also transfers to exact-solution benchmarks (LiveCodeBench, USACO) with 2-4% absolute gains, unlike raw-score baselines. The result suggests score-based heuristic tasks can serve as general-purpose RL training environments for coding ability.
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QUBRIC: Co-designing queries and rubrics for RL beyond verifiable rewards
QUBRIC is a framework that jointly optimizes queries and rubrics for reinforcement learning in settings where rewards are not strictly verifiable. The approach uses teacher-derived key points to rewrite open-ended queries into evaluable scenarios, applies contrastive rubric generation to capture teacher-policy gaps, and filters for learnability before GRPO training. Trained only on instruction-following data, QUBRIC achieves a +5.5 point gain on ArenaHard over an SFT baseline and transfers to legal, moral, and narrative reasoning benchmarks (+6.3 points average), suggesting rubric-based RL can complement RLVR in non-verifiable domains.
ExpRL: RL-based mid-training using human QA data as reward scaffolds for LLM reasoning
ExpRL proposes an automated approach to LLM mid-training that replaces manually curated reasoning traces with large corpora of human-written QA data used as reward scaffolds rather than imitation targets. Reference solutions are hidden from the policy and used only to construct problem-specific grading rubrics, enabling dense process-level rewards that reinforce partial progress and intermediate reasoning steps. On challenging math reasoning benchmarks, ExpRL outperforms SFT, sparse-reward GRPO, and self-distillation as an RL initialization strategy, with additional mixed-domain experiments suggesting broader applicability.
POW3R: Policy-Aware Rubric Rewards for More Efficient RLVR Training
This paper identifies a failure mode in rubric-based reinforcement learning with verifiable rewards (RLVR): static aggregation of criterion weights conflates human-assigned importance with current optimization utility, causing many criteria to be either already saturated or unreachable. The authors introduce POW3R, a framework that dynamically reweights criterion-level rewards during training using rollout-level contrast to emphasize criteria that currently differentiate policy outputs. Across three base policies and two datasets (multimodal and text-only), POW3R wins 24 of 30 comparisons on rubric reward and strict completion metrics, and reaches equivalent performance in 2.5–4× fewer training steps than vanilla GRPO with rubric rewards.
PROVE framework trains LLMs for multi-step tool use via stateful MCP environments and programmatic rewards
Researchers introduce PROVE (Programmatic Rewards On Verified Environments), a framework for training LLMs to orchestrate multi-step tool calls using reinforcement learning. The system includes a library of 20 stateful MCP servers with 343 tools, an automated data synthesis pipeline that grounds training queries in live server state, and a multi-component programmatic reward function requiring no judge model. Training four models (Qwen3-4B, Qwen3-8B, Qwen2.5-7B, Granite-4.1-8B) with ~13K examples yields gains of up to +10.2 on BFCL Multi-Turn, +6.8 on tau2-bench, and +6.5 on T-Eval, demonstrating consistent improvements in multi-step tool orchestration.
RELEX: Extrapolating LLM RLVR Training via Rank-1 Parameter Trajectories
This paper demonstrates that RLVR weight update trajectories are extremely low-rank and near-linearly predictable, with a rank-1 approximation capturing most downstream performance gains. The authors propose RELEX, a compute-efficient method that observes a short training window, estimates the rank-1 subspace, and extrapolates future checkpoints via linear regression—requiring no additional training. Evaluated on Qwen2.5-Math-1.5B, Qwen3-4B-Base, and Qwen3-8B-Base, RELEX matches or exceeds full RLVR performance using as few as 15% of training steps, and can extrapolate up to 10–20× beyond the observed prefix. The authors attribute the method's effectiveness to a denoising effect from rank-1 projection that discards stochastic optimization noise.
Two is better than one: A Collapse-free Multi-Reward RLIF Training Framework
This paper proposes a multi-reward reinforcement learning from internal feedback (RLIF) framework that decomposes training signals into an answer-level reward via cluster voting and a completion-level reward via token-wise self-certainty. To address reward hacking and entropy collapse common in single-reward RLIF, the authors introduce GDPO-based normalization and KL-Cov regularization targeting low-entropy token distributions. Evaluated on mathematical reasoning and code-generation benchmarks, the method achieves stability and performance approaching supervised RLVR methods without requiring external ground-truth supervision. The work advances scalable unsupervised RL training for LLM reasoning.
RACES framework enables recursive composition of verifiable RL environments for LLM reasoning generalization
RACES (Recursive Automated Composition for Environment Scaling) is a new framework that treats verifiable RL training environments as composable building blocks, automatically fusing them when input/output types match. The system implements 300 base environments and four composition operators (SEQUENTIAL, PARALLEL, SORT, SELECT) to generate diverse reasoning patterns at scale. Experiments show consistent gains on unseen benchmarks: DeepSeek-R1-Distill-Qwen-14B improves from 48.2 to 51.3 and Qwen3-14B from 58.8 to 61.1 averaged across six benchmarks. Notably, RACES achieves parity with 300 individual environments using only 50 base environments, suggesting strong efficiency gains over linear environment scaling.
OPERA: Perplexity-based RL alignment for open-ended reasoning tasks
OPERA (Objective Perplexity-based Reflective Alignment) proposes replacing LLM-as-a-judge reward models with intrinsic rewards derived from perplexity dynamics to stabilize RL training on open-ended tasks like creative writing. The method includes a cold-start data synthesis pipeline generating 20,000 reasoning trajectories using perplexity-prioritized rollouts. Applied to Qwen3-8B, OPERA claims state-of-the-art among open-source models on open-ended tasks, reportedly matching or exceeding Gemini 2.5 and MiniMax-M2.5 on some benchmarks.