Reward Function Design And Shaping For Complex Multi Objective Tasks

via “reward model training for reinforcement learning from human feedback (rlhf)”

Shanghai AI Lab's multilingual foundation model.

Unique: InternLM provides pre-trained reward models that can be fine-tuned on domain-specific preferences, reducing training time compared to training from scratch; integrates with XTuner for efficient fine-tuning

vs others: More accessible than building custom reward models from scratch; comparable to OpenAI's reward modeling approach but with full transparency and ability to customize for specific domains

via “reward model training with configurable loss functions”

Reinforcement learning from human feedback — SFT, DPO, PPO trainers for LLM alignment.

Unique: Supports multiple loss variants (Bradley-Terry, Elo, margin-based) with automatic hyperparameter suggestions based on dataset statistics, and includes built-in reward calibration utilities to estimate preference probabilities from scores

vs others: More flexible than monolithic reward models because it supports both regression and ranking objectives; better integrated with TRL's ecosystem than standalone reward modeling libraries because it shares data pipeline and chat template handling

via “reward function design and shaping for complex multi-objective tasks”

* ⭐ 02/2022: [Magnetic control of tokamak plasmas through deep reinforcement learning](https://www.nature.com/articles/s41586-021-04301-9%E2%80%A6)

Unique: Combines potential-based reward shaping with multi-objective weighting to balance lap time, safety, and race position, using domain knowledge about racing physics to structure rewards that guide learning without over-constraining agent behavior or creating conflicting gradient signals

vs others: Achieves better policy robustness than single-objective rewards (lap time only) by explicitly balancing safety and race performance, and better sample efficiency than inverse RL approaches by leveraging domain knowledge to structure rewards directly

via “reward shaping and curriculum learning for complex locomotion tasks”

* ⭐ 10/2022: [Discovering faster matrix multiplication algorithms with reinforcement learning (AlphaTensor)](https://www.nature.com/articles/s41586-022%20-05172-4)

Unique: Combines multi-component reward shaping with progressive curriculum learning, where task difficulty increases automatically as policy performance improves, enabling stable training toward complex locomotion objectives

vs others: Guides RL training toward natural, energy-efficient gaits by decomposing objectives into weighted reward components and progressively increasing difficulty, compared to sparse reward or single-objective approaches

via “reward function discovery via code generation (eureka extension)”

* ⏫ 10/2023: [Eureka: Human-Level Reward Design via Coding Large Language Models (Eureka)](https://arxiv.org/abs/2310.12931)

Unique: Generates reward functions as executable Python code rather than treating them as hyperparameters or learned models. The LLM learns to write code that captures task-relevant objectives by analyzing which reward functions led to better RL agent performance, enabling discovery of novel reward structures that humans might not manually design.

vs others: Eliminates manual reward engineering bottleneck in RL, enabling faster iteration and discovery of non-obvious reward structures. More flexible than inverse RL (which requires demonstrations) and more interpretable than learned reward models, though computationally expensive due to RL training cost per iteration.

via “reward design with language model guidance”

* ⏫ 03/2023: [Reward Design with Language Models](https://arxiv.org/abs/2303.00001)

Unique: RLPD integrates LM-based reward design as a first-class component with automatic validation against offline data, whereas prior work treats reward engineering as a separate manual step. This enables end-to-end specification of RL tasks from natural language to learned policies.

vs others: More flexible than hand-crafted rewards because LMs can express complex multi-objective specifications, and more reliable than pure inverse RL because rewards are validated against ground-truth offline trajectories before deployment

via “reward-function-configuration”