How to Apply Reinforcement Learning in LLM Development

🤔 What if, instead of using prompts, you could fine-tune LLMs to incorporate self-feedback and improvement mechanisms more effectively? Self-feedback and improvement have been shown to be highly beneficial for LLMs and agents, allowing them to reflect on their behavior and reasoning and correct their mistakes as more computational resources or interactions become available. The authors mention that frequently used test-time methods like prompt tuning and few-shot learning that are used for self-improvement, often fail to enable models to correct their mistakes in complex reasoning tasks. ⛳ The paper introduces RISE: Recursive Introspection, an approach to improve LLMs by teaching them how to introspect and improve their responses iteratively. ⛳ RISE leverages principles from online imitation learning and reinforcement learning to develop a self-improvement mechanism within LLMs. By treating each prompt as part of a multi-turn Markov decision process (MDP), RISE allows models to learn from their previous attempts and refine their answers over multiple turns, ultimately improving their problem-solving capabilities. ⛳It models the fine-tuning process as a multi-turn Markov decision process, where the initial state is the prompt, and subsequent states involve recursive improvements. ⛳It employs a reward-weighted regression (RWR) objective to learn from both high- and low-quality rollouts, enabling models to improve over turns. The approach uses data generated by the learner itself or more capable models to supervise improvements iteratively. RISE significantly improves the performance of LLMs like LLaMa2, LLaMa3, and Mistral on math reasoning tasks, outperforming single-turn strategies with the same computational resources. Link: https://lnkd.in/e2JDQr8M

One of the biggest barriers to deploying LLM-based agents in real workflows is their poor performance on long-horizon reasoning. Agents often generate coherent short responses but struggle when a task requires planning, tool use, or multi-step decision-making. The issue is not just accuracy at the end, but the inability to reason through the middle. Without knowing which intermediate steps helped or hurt, agents cannot learn to improve. This makes long-horizon reasoning one of the hardest and most unsolved problems for LLM generalization. It is relatively easy for a model to retrieve a document, answer a factual question, or summarize a short email. It is much harder to solve a billing dispute that requires searching, interpreting policy rules, applying edge cases, and adjusting the recommendation based on prior steps. Today’s agents can generate answers, but they often fail to reflect, backtrack, or reconsider earlier assumptions. A new paper from Google DeepMind and Stanford addresses this gap with a method called SWiRL: Step-Wise Reinforcement Learning. Rather than training a model to get the final answer right, SWiRL trains the model to improve each step in a reasoning chain. It does this by generating synthetic multi-step problem-solving traces, scoring every individual step using a reward model (Gemini 1.5 Pro), and fine-tuning the base model to favor higher-quality intermediate steps. This approach fundamentally changes the way we train reasoning agents. Instead of optimizing for final outcomes, the model is updated based on how good each reasoning step was in context. For example, if the model generates a search query or a math step that is useful, even if the final answer is wrong, that step is rewarded and reinforced. Over time, the agent learns not just to answer, but to reason more reliably. This is a major departure from standard RLHF, which only gives feedback at the end. SWiRL improves performance by 9.2 percent on HotPotQA, 16.9 percent on GSM8K when trained on HotPotQA, and 11 to 15 percent on other multi-hop and math datasets like MuSiQue, BeerQA, and CofCA. It generalizes across domains, works without golden labels, and outperforms both supervised fine-tuning and single-step RL methods. The implications are substantial: we can now train models to reason better by scoring and optimizing their intermediate steps. Better reward models, iterative reflection, tool-assisted reasoning, and trajectory-level training will lead to more robust performance in multi-step tasks. This is not about mere performance improvement. It shows how we can begin to train agents not to mimic outputs, but to improve the quality of their thought process. That’s essential if we want to build agents that work through problems, adapt to new tasks, and operate autonomously in open-ended environments.

🧠 [Primer] Reinforcement Fine-Tuning (RFT) • http://rft.aman.ai - 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐑𝐅𝐓? RFT is a modern approach for tailoring LLMS using reward-based feedback instead of large labeled datasets as in the case of Supervised Fine-Tuning (SFT). It shines in tasks where correctness can be programmatically verified—like code generation, mathematical reasoning, etc.—allowing models to improve performance and reasoning even with minimal data. RFT shifts the focus from memorization (as in SFT) to dynamic learning, enabling LLMs to discover and optimize their own strategies based on what works. - 𝐊𝐞𝐲 𝐀𝐝𝐯𝐚𝐧𝐭𝐚𝐠𝐞𝐬/𝐔𝐬𝐞-𝐂𝐚𝐬𝐞𝐬: RFT enables data-efficient fine-tuning, often requiring as few as 10–100 examples, making it ideal for low-resource scenarios . It promotes better generalization, enhances chain-of-thought reasoning, and most importantly -- its compatibility with automated grading systems also reduces reliance on human annotations. 🔹 Supervised Fine-Tuning vs. Reinforcement Fine-Tuning   🔹 When Should You Use SFT vs. RFT?   🔹 How RFT Works    • Dataset Construction    • The Role of the Grader     - Direct Comparison     - Heuristic Evaluation     - LLM-based Grading     - Grader Strategies     - Grader Architectures     - Anti-Reward Hacking Mechanisms    • Preventing Overfitting    • The Training Loop    • Improving RFT Performance  🔹 Case Study: Predibase's Python-to-Triton Transpilation    • Why Use RFT for This Task?    • System & User Messages    • Rewards (Formatting, Compilation, Correctness)  • Anti-Reward Hacking Mechanisms    • Putting GRPO and LoRA Together  • Training Dynamics and Scaling   🔹 Advantages of RFT    • Data Efficiency    • Improved Reasoning Capabilities    • Domain-Specific Optimization    • Cost Efficiency   🔹 Real-World Applications of RFT    • GPU Kernel Code Generation    • Legal AI Assistants    • Financial Risk Assessment    • Scientific Research and Rare Disease Diagnosis   🔹Reinforcement Learning with Verifiable Rewards (RLVR)  • Comparative Analysis: RFT vs. RLVR Primer written in collaboration with Vinija Jain. #artificialintelligence #llms #agents