DoorDash deploys multi-agent RL system for adaptive dispatch objective weights in food-delivery marketplace
Researchers at DoorDash present a deployed reinforcement learning system that adapts dispatch objective weights in a three-sided food-delivery marketplace using delayed operational feedback signals. Rather than replacing the combinatorial optimizer, a store-level policy selects discrete multipliers that shift the optimizer's tradeoff between delivery quality and batching efficiency. The system uses centralized offline training with Double Q-learning and a conservative regularizer to handle out-of-distribution overestimation, then executes decentrally per store. A production switchback experiment shows increased batching and reduced courier time costs without degrading customer delivery quality.
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Scaling Up Reinforcement Learning for Traffic Smoothing: A 100-AV Highway Deployment
Berkeley AI Research (BAIR) deployed 100 RL-controlled autonomous vehicles into real rush-hour highway traffic on Interstate 24 near Nashville to dampen stop-and-go waves and reduce fuel consumption. The RL controllers were trained in data-driven simulations built from real highway trajectory data, using only local sensor inputs (speed, lead vehicle speed, gap) to enable decentralized deployment on standard vehicles. Reward design balanced wave smoothing, energy efficiency, safety, comfort, and adherence to human driving norms. The paper documents the sim-to-real transfer challenges encountered during this large-scale field experiment.
AgenticRL: Self-refining LLM-guided reward design and policy refinement for UAV navigation
AgenticRL is a framework that uses a multimodal GPT agent to automate reward function generation, policy training via PPO, and closed-loop self-refinement for UAV navigation tasks. The agent evaluates trained policies through diagnostic feedback, identifies failure modes, and iteratively refines rewards without human intervention. Evaluated across five navigation tasks, the closed-loop refinement improves policy behavior by 71% over initial rewards, with sim-to-real transfer achieving 91% real-world success rate and 94% sim-to-real accuracy.
HABC: Hierarchical Advantage Weighting for Online RL Fine-Tuning of Vision-Language-Action Policies
Researchers introduce Hierarchical Advantage-Weighted Behavior Cloning (HABC), a method for fine-tuning pretrained Vision-Language-Action (VLA) policies via online RL using only sparse binary episode outcomes. HABC trains separate critic heads for viability and efficiency objectives, combines them via a state-adaptive gate, and applies intervention-aware credit assignment to avoid incorrect supervision across human-intervention boundaries. On three contact-rich bimanual real-robot tasks, HABC improves success rates from SFT baselines of 36%, 44%, and 12% to 92%, 88%, and 38% respectively. The work addresses a fundamental credit assignment problem in robot learning from sparse outcome signals.
DRPO: Smooth divergence regularization replaces hard masking in LLM RL training
A new arXiv preprint proposes Divergence Regularized Policy Optimization (DRPO), a method that replaces the hard trust-region mask used in DPPO with a smooth advantage-weighted quadratic regularizer on policy shift. The approach addresses a known weakness in PPO and GRPO where importance ratios poorly proxy distributional shift in long-tailed vocabularies, and in DPPO where gradient signals are discarded rather than corrected at trust-region boundaries. Experiments across model scales, architectures, and precision settings show improved stability and efficiency in LLM RL post-training.
DRIFT: Decoupled Rollouts and Importance-Weighted Fine-Tuning for Efficient Multi-Turn Optimization
DRIFT is a training framework that bridges online RL and offline SFT for multi-turn LLM optimization by exploiting the theoretical equivalence between KL-regularized RL and importance-weighted supervised learning. It decouples rollout generation from policy optimization: trajectories are sampled from a fixed reference policy offline, weighted by return-based importance scores, and used for weighted SFT. Empirically, DRIFT matches or exceeds multi-turn RL baselines while retaining the efficiency and simplicity of standard supervised fine-tuning. Code is publicly released.
STARE: Token-level advantage reweighting to prevent entropy collapse in GRPO-style RL training
Researchers introduce STARE, a method addressing policy entropy collapse in GRPO-style reinforcement learning from verifiable rewards (RLVR) for LLM post-training. Through first-order gradient analysis, they identify a token-level credit assignment mismatch and propose selectively reweighting advantages for entropy-critical tokens using batch-internal surprisal quantiles plus a closed-loop entropy gate. Evaluated across 1.5B–32B models on short/long chain-of-thought and multi-turn tool use tasks, STARE outperforms DAPO and other baselines by 4–8% on AIME24/25 while sustaining stable training over thousands of steps.
Finetune Stable Diffusion Models with DDPO via TRL
Hugging Face's TRL library adds support for DDPO (Denoising Diffusion Policy Optimization), enabling reinforcement learning-based finetuning of Stable Diffusion models. This extends TRL's RLHF tooling beyond language models to image generation, allowing reward-driven optimization of diffusion models. The post demonstrates practical usage of the new DDPO trainer within the TRL ecosystem.
Learning from Human Preferences: OpenAI and DeepMind Collaborate on Reward Learning from Comparisons
OpenAI, in collaboration with DeepMind's safety team, published a method for learning reward functions directly from human preference comparisons between pairs of agent behaviors, eliminating the need to hand-code goal functions. The algorithm infers human intent by asking evaluators which of two proposed behaviors is preferable, addressing risks from misspecified reward functions. This work is an early foundational contribution to what would become reinforcement learning from human feedback (RLHF). It targets both safety and alignment concerns around reward hacking and proxy gaming.