Reinforcement Learning with Verifiable Rewards
reinforcement-learning-with-verifiable-rewards-cc30a5c2·8 events·first seen 1mo agoAliases: Reinforcement Learning with Verifiable Rewards, Reinforcement Learning with Verifiable Rewards (RLVR), Reinforcement Learning from Verifiable Rewards, RLVR (Reinforcement Learning from Verifiable Rewards), RLVR (Reinforcement Learning with Verifiable Rewards)
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DelTA: Discriminative Token Credit Assignment for RLVR Training
DelTA introduces a discriminative token credit assignment method for reinforcement learning from verifiable rewards (RLVR) that addresses the problem of high-frequency formatting tokens dominating policy gradient updates. The method estimates per-token coefficients to amplify side-specific gradient directions and downweight shared or weakly discriminative ones, making the effective update direction more contrastive. On seven mathematical benchmarks, DelTA outperforms same-scale baselines by 3.26 and 2.62 average points on Qwen3-8B-Base and Qwen3-14B-Base respectively, with additional gains on code generation tasks.
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.
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.
LongTraceRL: Reinforcement Learning for Long-Context Reasoning via Search Agent Trajectories and Rubric Rewards
LongTraceRL is a new RL training framework for improving long-context reasoning in LLMs, addressing limitations of existing RLVR methods. It constructs challenging training data using multi-hop questions from knowledge graph random walks and tiered distractors derived from search agent trajectories (high-confusability: read but uncited; low-confusability: seen but unopened). A rubric reward provides entity-level process supervision along reasoning chains, applied only to correct responses to prevent reward hacking. Experiments across three LLMs (4B–30B parameters) on five long-context benchmarks show consistent improvements over strong baselines.
Ecom-RLVE: Adaptive Verifiable Environments for E-Commerce Conversational Agents
Hugging Face published a blog post introducing Ecom-RLVE, a framework for training e-commerce conversational agents using reinforcement learning with verifiable environments. The approach creates adaptive environments that can verify agent actions and outcomes in e-commerce contexts, enabling RL-based training signals. This represents an application of the RLVR (Reinforcement Learning with Verifiable Rewards) paradigm to a specific commercial domain.
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.
DistIL: Distributional DAgger for RL from Rich Feedback beyond single-bit rewards
A new arXiv preprint introduces DistIL, a distributional variant of the DAgger imitation learning algorithm designed to exploit rich feedback signals (execution traces, tool outputs, expert corrections) rather than the single-bit correctness reward used in standard RLVR. The method uses a forward cross-entropy objective that provides monotonic policy improvement guarantees, unlike reverse KL or Jensen-Shannon divergence objectives used in prior self-distillation approaches. Empirically, DistIL outperforms RLVR and self-distillation baselines on scientific reasoning, coding, and hard math benchmarks.
Are Full Rollouts Necessary for On-Policy Distillation?
This paper investigates whether full rollouts are required during on-policy distillation (OPD) for training reasoning models, identifying rollout horizon as a key computational bottleneck. The authors propose two strategies: Progressive OPD (POPD), which gradually expands rollout horizon during training, and Truncated OPD (TOPD), which uses permanently truncated rollouts. Experiments on mathematical reasoning show POPD achieves up to 3× training efficiency improvement, while TOPD matches full OPD performance using only 10% of the rollout horizon, yielding significant wall-clock and memory savings.